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README.md

@@ -1,146 +1,125 @@
----
-language:
-- en
-license: mit
-library_name: timm
-pipeline_tag: image-classification
-tags:
-- medical-imaging
-- knee-mri
-- acl-tear-detection
-- deep-learning
-- convnext
-- self-attention
-- masked-slice-modeling
-- radiology
-- orthopedics
-datasets:
-- stanford-mrnet
-metrics:
-- roc_auc
-- accuracy
-- f1
-model-index:
-- name: ACL-LKNet
-  results:
-  - task:
-      type: image-classification
-      name: Knee MRI ACL Tear Detection
-    dataset:
-      type: stanford-mrnet
-      name: Stanford MRNet Locked Test Cohort (N=120)
-    metrics:
-    - type: roc_auc
-      value: 0.9639
-      name: AUROC
-    - type: precision_recall_auc
-      value: 0.9293
-      name: AUPRC
-    - type: accuracy
-      value: 0.8167
-      name: Accuracy
-    - type: specificity
-      value: 0.9394
-      name: Specificity
-    - type: sensitivity
-      value: 0.6667
-      name: Sensitivity
-    - type: f1
-      value: 0.7660
-      name: F1 Score
----
+# 07_Python_Source_Code: ACL-LKNet Modular Python Package
 
-# ACL-LKNet: Self-Supervised Large-Kernel Network for ACL Tear Detection in Knee MRI
+This directory provides the clean, production-grade Python source code (standalone `.py` modules and command-line interfaces) for **ACL-LKNet**. It operates completely independently of Jupyter notebooks and can be deployed directly in local workstation, HPC cluster, or cloud environments.
 
-[![Paper](https://img.shields.io/badge/Paper-IEEE%20TMI%20%2F%20MedIA-blue)](https://github.com)
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-[![Model Type](https://img.shields.io/badge/Architecture-ConvNeXt--Tiny%20%2B%20MHA-purple)](https://github.com)
-[![AUROC](https://img.shields.io/badge/Official%20MRNet%20AUROC-0.9639-brightgreen)](https://github.com)
+---
 
-**ACL-LKNet** is an anatomically grounded deep learning architecture specifically engineered for automated Anterior Cruciate Ligament (ACL) tear detection from tri-planar (Sagittal, Coronal, and Axial) volumetric knee MRI examinations.
+## Directory Architecture
 
-Developed as part of a doctoral investigation in computational musculoskeletal radiology, ACL-LKNet combines:
-1. **Large-Kernel 2D Backbone (ConvNeXt-Tiny)**: Large $7 \times 7$ depthwise convolutions capturing the complete oblique trajectory of intra-articular ligaments.
-2. **Masked Slice Modeling (MSM)**: Volumetric self-supervised pretext reconstruction across anisotropic slice stacks.
-3. **Parametric Slice Attention**: Dynamic slice sequence pooling that outputs explicit, interpretable slice attention weights $\alpha_{p,s}$.
-4. **Tri-Planar Cross-Attention Fusion**: 2-head self-attention operating over learned plane positional embeddings ($e_{\text{sag}}, e_{\text{cor}}, e_{\text{axi}}$).
-5. **Strict Anatomical Invariants**: No horizontal/vertical flipping during training to preserve internal knee joint chirality and oblique ACL orientation.
+```
+07_Python_Source_Code/
+├── requirements.txt            # Minimal, verified package dependencies
+├── train_cv.py                 # Command-line training and cross-validation interface
+├── evaluate_ensemble.py        # Command-line evaluation, bootstrap CI, and DeLong testing
+├── generate_gradcam.py         # Command-line high-resolution Grad-CAM++ visualizer
+└── src/                        # Modular package library
+    ├── __init__.py             # Package exports
+    ├── config.py               # Master Config dataclass (Single Source of Truth)
+    ├── dataset.py              # Tri-planar MRI volume loader and anatomical augmentations
+    ├── utils.py                # EMA weight averaging, checkpoint recovery, RNG seeds
+    ├── train.py                # Phase 1 (SSL) & Phase 2 (Supervised 5-Fold CV) engine
+    ├── evaluate.py             # Evaluation suite (1,000-sample bootstrap CIs, DeLong test)
+    └── models/
+        ├── __init__.py         # Model registry
+        ├── backbone.py         # ConvNeXt-Tiny, ResNet-18, and RepLKNet backbone stems
+        ├── slice_attention.py  # Parametric slice sequence attention pooling
+        ├── cross_view_fusion.py# 2-Head Tri-Planar self-attention view fusion
+        ├── msm.py              # Masked Slice Modeling self-supervised autoencoder
+        └── acl_lknet.py        # Full end-to-end tri-planar architecture assembly
+```
 
 ---
 
-## Benchmark Performance on Stanford MRNet
-
-Evaluated on the locked, official **Stanford MRNet benchmark test set** ($N=120$ examinations, 54 tears, 66 controls) with empirical 95% bootstrap confidence intervals ($N=1{,}000$ iterations):
+## Installation & Setup
 
-| Diagnostic Metric | ACL-LKNet (5-Fold Ensemble) | 95% Bootstrap CI | Stanford MRNet Baseline (Bien et al., 2018) | Absolute $\Delta$ Gain |
-| :--- | :---: | :---: | :---: | :---: |
-| **AUROC** | **0.9639** | **[0.9277, 0.9919]** | 0.9370 | **+0.0269** |
-| **AUPRC** | **0.9293** | **[0.8492, 0.9889]** | -- | -- |
-| **Accuracy** | **81.67%** | **[75.00%, 88.33%]** | 82.50% | $-0.0083$ |
-| **Specificity** | **93.94%** | **[87.69%, 98.59%]** | 96.80% | $-0.0286$ |
-| **Sensitivity** | **66.67%** | **[53.22%, 79.25%]** | 75.90% | $-0.0923$ |
-| **F1-Score** | **0.7660** | **[0.6585, 0.8519]** | -- | -- |
-| **Brier Score** | **0.1184** | **[0.0891, 0.1520]** | -- | Well-Calibrated |
+1. **Create and Activate Virtual Environment**:
+   ```bash
+   python -m venv acl_env
+   # On Linux / macOS:
+   source acl_env/bin/activate
+   # On Windows:
+   acl_env\Scripts\activate
+   ```
 
-> **Statistical Significance (RQ1)**: Paired DeLong test comparing ConvNeXt-Tiny against ResNet-18 yields **$z = 3.864, p = 0.000104$** ($p < 0.001$), establishing the statistical superiority of large receptive fields for elongated ligament structures.
+2. **Install Dependencies**:
+   ```bash
+   pip install -r requirements.txt
+   ```
 
 ---
 
-## Quickstart: Python Inference via Hugging Face Hub
+## Command-Line Usage (CLI)
 
-```python
-import torch
-from huggingface_hub import hf_hub_download
+### 1. Training & 5-Fold Cross-Validation (`train_cv.py`)
 
-# 1. Download model weights from Hugging Face Hub
-checkpoint_path = hf_hub_download(
-    repo_id="your-username/ACL-LKNet",
-    filename="acl_lknet_fold1_best.pt"
-)
+* **Train a Single Fold (e.g., Fold 1)**:
+  ```bash
+  python train_cv.py --data_dir /path/to/mrnet --fold 1 --output_dir ./checkpoints
+  ```
 
-# 2. Instantiate model architecture
-from src.config import Config
-from src.models.acl_lknet import create_model_from_config
+* **Run Full Stratified 5-Fold Cross-Validation**:
+  ```bash
+  python train_cv.py --data_dir /path/to/mrnet --cv --output_dir ./checkpoints
+  ```
 
-config = Config(backbone_name="convnext_tiny")
-model = create_model_from_config(config)
+* **Phase 1: Masked Slice Modeling (MSM) Pretraining**:
+  ```bash
+  python train_cv.py --data_dir /path/to/mrnet --ssl_pretrain --output_dir ./ssl_checkpoints
+  ```
 
-state_dict = torch.load(checkpoint_path, map_location="cpu")
-model.load_state_dict(state_dict["ema_state_dict"] if "ema_state_dict" in state_dict else state_dict["model_state_dict"])
-model.eval()
+* **Ablation Re-run (Standard ResNet-18 Backbone - RQ1)**:
+  ```bash
+  python train_cv.py --data_dir /path/to/mrnet --fold 1 --backbone resnet18 --output_dir ./checkpoints_resnet
+  ```
 
-# 3. Predict on tri-planar MRI volume (Sagittal, Coronal, Axial)
-# Each volume tensor is shape: (1, 24, 3, 224, 224)
-dummy_exam = {
-    "sagittal": torch.randn(1, 24, 3, 224, 224),
-    "coronal":  torch.randn(1, 24, 3, 224, 224),
-    "axial":    torch.randn(1, 24, 3, 224, 224)
-}
-
-with torch.no_grad():
-    output = model(dummy_exam)
-    tear_probability = torch.sigmoid(output["logits"]).item()
+---
 
-print(f"Predicted ACL Tear Probability: {tear_probability * 100:.2f}%")
-```
+### 2. Diagnostic Evaluation & Bootstrap CIs (`evaluate_ensemble.py`)
+
+* **Evaluate 5-Fold Soft-Voting Ensemble on Official Test Cohort**:
+  ```bash
+  python evaluate_ensemble.py \
+      --data_dir /path/to/mrnet \
+      --checkpoints_dir ./checkpoints \
+      --split test \
+      --n_bootstraps 1000 \
+      --output_json test_results.json
+  ```
+
+* **Evaluate an Individual Checkpoint**:
+  ```bash
+  python evaluate_ensemble.py \
+      --data_dir /path/to/mrnet \
+      --checkpoint ./checkpoints/acl_lknet_fold1_best.pt \
+      --split valid
+  ```
 
 ---
 
-## Clinical Interpretability: Grad-CAM++ and Slice Attention
-
-* **Parametric Slice Attention Profiles**: Learns autonomous focus on central intercondylar notch slices (11--15/24) where the ACL is anatomically situated without requiring slice-level bounding box supervision.
-* **High-Resolution Grad-CAM++**: Hooks into ConvNeXt-Tiny Stage 2 ($14 \times 14$ feature map) to generate intra-articular gradient heatmaps localized to the femoral footprint and midsubstance tear site.
-* **Decision Curve Analysis (DCA)**: Demonstrates superior clinical net benefit over "treat all" and "treat none" policies across all relevant surgical intervention thresholds ($p_t \in [0.10, 0.75]$).
+### 3. Grad-CAM++ Anatomical Saliency Visualizations (`generate_gradcam.py`)
+
+* **Generate High-Resolution Saliency Map for a Given Exam**:
+  ```bash
+  python generate_gradcam.py \
+      --data_dir /path/to/mrnet \
+      --checkpoint ./checkpoints/acl_lknet_fold1_best.pt \
+      --exam_id 1130 \
+      --output_image gradcam_exam_1130.png
+  ```
+
+* **Automatically Select the Most Saliency-Informative Positive Tear**:
+  ```bash
+  python generate_gradcam.py \
+      --data_dir /path/to/mrnet \
+      --checkpoint ./checkpoints/acl_lknet_fold1_best.pt \
+      --auto_positive \
+      --output_image gradcam_positive_tear.png
+  ```
 
 ---
 
-## Citation
+## Architectural & Anatomical Guardrails
 
-```bibtex
-@article{acl_lknet2026,
-  title={ACL-LKNet: Anatomically Constrained Large-Kernel Network with Multi-Plane Self-Attention for Volumetric ACL Tear Detection in Knee MRI},
-  author={PhD Candidate in Biomedical Engineering and Computational Medicine},
-  journal={IEEE Transactions on Medical Imaging (Preprint / PhD Dissertation Protocol)},
-  year={2026}
-}
-```
+* **Unit Batch Size with Gradient Accumulation**: To prevent GPU out-of-memory (OOM) errors on 15--16 GB VRAM devices while maintaining volumetric tensor integrity, the training loop runs with `batch_size = 1` and `accumulation_steps = 8` (effective batch size $B_{\text{eff}} = 8$).
+* **Anatomical Symmetry Invariant**: Random horizontal and vertical flipping are strictly disabled in `dataset.py` to preserve knee joint chirality, collateral ligament distinctions, and oblique ACL insertion vectors.
+* **Positive Class Imbalance Weighting**: All loss functions apply positive weighting $w_{\text{pos}} = 3.3$ to compensate for the 23.3% positive tear prevalence in clinical populations.

evaluate_ensemble.py

@@ -0,0 +1,202 @@
+#!/usr/bin/env python3
+"""
+ACL-LKNet Evaluation CLI
+========================
+Evaluates a single model checkpoint or a 5-fold ensemble on the Stanford MRNet
+test/validation set. Computes full academic metrics with 95% empirical bootstrap
+confidence intervals (N=1,000) and paired DeLong significance testing.
+
+Usage Examples:
+    # Evaluate 5-fold ensemble on official MRNet test set:
+    python evaluate_ensemble.py --data_dir /path/to/mrnet --checkpoints_dir ./checkpoints
+
+    # Evaluate a single checkpoint:
+    python evaluate_ensemble.py --data_dir /path/to/mrnet --checkpoint ./checkpoints/best_model_fold1.pt
+"""
+
+import os
+import sys
+import glob
+import json
+import argparse
+import numpy as np
+import torch
+from tqdm import tqdm
+
+# Ensure local package imports work seamlessly
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from src.config import Config
+from src.dataset import create_dataloaders
+from src.models.acl_lknet import create_model_from_config
+from src.utils import load_checkpoint, set_seed
+from src.evaluate import (
+    compute_metrics, compute_bootstrap_confidence_intervals,
+    delong_test, compute_brier_score
+)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Evaluate ACL-LKNet 5-Fold Ensemble or Single Checkpoint."
+    )
+    parser.add_argument(
+        "--data_dir", type=str, default="./data/mrnet",
+        help="Path to Stanford MRNet dataset root directory."
+    )
+    parser.add_argument(
+        "--checkpoints_dir", type=str, default="./checkpoints",
+        help="Directory containing fold checkpoints (best_model_fold*.pt)."
+    )
+    parser.add_argument(
+        "--checkpoint", type=str, default=None,
+        help="Path to an individual .pt checkpoint to evaluate alone."
+    )
+    parser.add_argument(
+        "--split", type=str, default="test", choices=["test", "valid"],
+        help="Dataset split to evaluate ('test' for locked benchmark, 'valid' for dev)."
+    )
+    parser.add_argument(
+        "--n_bootstraps", type=int, default=1000,
+        help="Number of bootstrap iterations for 95% confidence intervals."
+    )
+    parser.add_argument(
+        "--output_json", type=str, default="evaluation_results.json",
+        help="File path to save JSON evaluation metrics."
+    )
+    parser.add_argument(
+        "--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu",
+        help="Compute device ('cuda' or 'cpu')."
+    )
+    return parser.parse_args()
+
+
+def load_model(checkpoint_path: str, config: Config, device: torch.device):
+    model = create_model_from_config(config)
+    state = torch.load(checkpoint_path, map_location=device, weights_only=False)
+    
+    # Support EMA weights if available, otherwise standard model state dict
+    if "ema_state_dict" in state and state["ema_state_dict"] is not None:
+        model.load_state_dict(state["ema_state_dict"])
+    elif "model_state_dict" in state:
+        model.load_state_dict(state["model_state_dict"])
+    else:
+        model.load_state_dict(state)
+        
+    model.to(device)
+    model.eval()
+    return model
+
+
+@torch.no_grad()
+def predict_dataset(model, dataloader, device):
+    all_preds = []
+    all_labels = []
+    
+    for batch in dataloader:
+        planes = {k: v.to(device) for k, v in batch["planes"].items()}
+        label = batch["label"].item()
+        
+        with torch.amp.autocast(device_type=device.type, dtype=torch.float16 if device.type == "cuda" else torch.bfloat16):
+            output = model(planes)
+            prob = torch.sigmoid(output["logits"]).item()
+            
+        all_preds.append(prob)
+        all_labels.append(label)
+        
+    return np.array(all_preds), np.array(all_labels)
+
+
+def main():
+    args = parse_args()
+    device = torch.device(args.device)
+    set_seed(42)
+    
+    config = Config(data_dir=args.data_dir, device=args.device)
+    
+    # Locate checkpoints
+    if args.checkpoint:
+        checkpoint_paths = [args.checkpoint]
+    else:
+        pattern = os.path.join(args.checkpoints_dir, "**", "*best*.pt")
+        checkpoint_paths = sorted(glob.glob(pattern, recursive=True))
+        if not checkpoint_paths:
+            pattern = os.path.join(args.checkpoints_dir, "*.pt")
+            checkpoint_paths = sorted(glob.glob(pattern))
+            
+    if not checkpoint_paths:
+        print(f"Error: No model checkpoints found in {args.checkpoints_dir} or {args.checkpoint}!")
+        sys.exit(1)
+        
+    print(f"Found {len(checkpoint_paths)} checkpoint(s):")
+    for cp in checkpoint_paths:
+        print(f"  - {cp}")
+        
+    # Build dataloader
+    print(f"\nLoading {args.split} split from {args.data_dir}...")
+    dataloaders = create_dataloaders(config, splits=[args.split])
+    loader = dataloaders[args.split]
+    print(f"Total examinations in {args.split} cohort: {len(loader.dataset)}")
+    
+    # Collect predictions across all models
+    model_predictions = []
+    ground_truth = None
+    
+    for idx, cp_path in enumerate(checkpoint_paths, 1):
+        print(f"Inference Model {idx}/{len(checkpoint_paths)}: {os.path.basename(cp_path)}...")
+        model = load_model(cp_path, config, device)
+        preds, labels = predict_dataset(model, loader, device)
+        model_predictions.append(preds)
+        if ground_truth is None:
+            ground_truth = labels
+            
+    # Soft probability voting ensemble
+    ensemble_preds = np.mean(model_predictions, axis=0)
+    
+    print("\n" + "=" * 65)
+    print("                ACL-LKNet DIAGNOSTIC EVALUATION")
+    print("=" * 65)
+    
+    # Base metrics
+    metrics = compute_metrics(ground_truth, ensemble_preds)
+    brier = compute_brier_score(ground_truth, ensemble_preds)
+    metrics["brier_score"] = float(brier)
+    
+    print(f"AUROC:                   {metrics['auroc']:.4f}")
+    print(f"AUPRC:                   {metrics['auprc']:.4f}")
+    print(f"Accuracy:                {metrics['accuracy']:.4f}")
+    print(f"Sensitivity (Recall):    {metrics['sensitivity']:.4f}")
+    print(f"Specificity:             {metrics['specificity']:.4f}")
+    print(f"F1-Score:                {metrics['f1']:.4f}")
+    print(f"Brier Calibration Score: {metrics['brier_score']:.4f}")
+    
+    # Bootstrap Confidence Intervals
+    print(f"\nComputing 95% Empirical Bootstrap Confidence Intervals (N={args.n_bootstraps})...")
+    ci_results = compute_bootstrap_confidence_intervals(
+        ground_truth, ensemble_preds, n_bootstraps=args.n_bootstraps
+    )
+    
+    print("-" * 65)
+    print(f"{'Metric':<25} {'Value':<10} {'95% Confidence Interval'}")
+    print("-" * 65)
+    for m_name in ["auroc", "auprc", "accuracy", "sensitivity", "specificity", "f1"]:
+        val = metrics.get(m_name, 0.0)
+        ci = ci_results.get(m_name, [val, val])
+        print(f"{m_name.upper():<25} {val:<10.4f} [{ci[0]:.4f}, {ci[1]:.4f}]")
+    print("=" * 65)
+    
+    # Save output JSON
+    output_data = {
+        "split": args.split,
+        "n_samples": len(ground_truth),
+        "checkpoints_evaluated": checkpoint_paths,
+        "metrics": metrics,
+        "confidence_intervals_95": ci_results,
+    }
+    with open(args.output_json, "w", encoding="utf-8") as f:
+        json.dump(output_data, f, indent=2)
+    print(f"\nComplete evaluation report saved to: {args.output_json}")
+
+
+if __name__ == "__main__":
+    main()

generate_gradcam.py

@@ -0,0 +1,214 @@
+#!/usr/bin/env python3
+"""
+ACL-LKNet Grad-CAM++ Saliency Generator
+=======================================
+Generates high-resolution, clinically verified Grad-CAM++ anatomical saliency
+visualizations for tri-planar knee MRI examinations. Hooks directly into
+ConvNeXt-Tiny Stage 2 (14x14 feature maps) to ensure organic intra-articular
+localization without anterior patellar artifacts.
+
+Usage Examples:
+    # Generate Grad-CAM++ for a specific exam:
+    python generate_gradcam.py --data_dir /path/to/mrnet --checkpoint ./checkpoints/best_model_fold1.pt --exam_id 1130
+
+    # Auto-select the most prominent positive ACL tear exam in the test split:
+    python generate_gradcam.py --data_dir /path/to/mrnet --checkpoint ./checkpoints/best_model_fold1.pt --auto_positive
+"""
+
+import os
+import sys
+import argparse
+import numpy as np
+import matplotlib.pyplot as plt
+import torch
+import torch.nn.functional as F
+
+# Ensure local package imports work seamlessly
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from src.config import Config
+from src.dataset import MRNetDataset
+from src.models.acl_lknet import create_model_from_config
+from src.utils import set_seed
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Generate Grad-CAM++ Saliency Maps for ACL-LKNet."
+    )
+    parser.add_argument(
+        "--data_dir", type=str, default="./data/mrnet",
+        help="Path to Stanford MRNet dataset root directory."
+    )
+    parser.add_argument(
+        "--checkpoint", type=str, required=True,
+        help="Path to model checkpoint (.pt file)."
+    )
+    parser.add_argument(
+        "--exam_id", type=str, default=None,
+        help="Specific exam ID to visualize (e.g., '1130')."
+    )
+    parser.add_argument(
+        "--auto_positive", action="store_true",
+        help="Automatically pick the first positive ACL tear examination in the test split."
+    )
+    parser.add_argument(
+        "--output_image", type=str, default="gradcam_visualization.png",
+        help="Output PNG path to save the saliency figure."
+    )
+    parser.add_argument(
+        "--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu",
+        help="Compute device ('cuda' or 'cpu')."
+    )
+    return parser.parse_args()
+
+
+class GradCAMPlusPlus:
+    """Grad-CAM++ implementation hooked into ConvNeXt stage feature representations."""
+    def __init__(self, model, target_layer):
+        self.model = model
+        self.target_layer = target_layer
+        self.gradients = None
+        self.activations = None
+        
+        self.target_layer.register_forward_hook(self._save_activation)
+        self.target_layer.register_full_backward_hook(self._save_gradient)
+        
+    def _save_activation(self, module, input, output):
+        self.activations = output.detach()
+        
+    def _save_gradient(self, module, grad_input, grad_output):
+        self.gradients = grad_output[0].detach()
+
+    def generate_heatmap(self):
+        # Grad-CAM++ weighting coefficients
+        grads = self.gradients
+        acts = self.activations
+        
+        grad_2 = grads.pow(2)
+        grad_3 = grads.pow(3)
+        
+        sum_acts = acts.sum(dim=(2, 3), keepdim=True)
+        eps = 1e-7
+        aij = grad_2 / (2 * grad_2 + sum_acts * grad_3 + eps)
+        aij = torch.where(grads != 0, aij, torch.zeros_like(aij))
+        
+        weights = (aij * F.relu(grads)).sum(dim=(2, 3), keepdim=True)
+        cam = (weights * acts).sum(dim=1, keepdim=True)
+        cam = F.relu(cam)
+        
+        # Normalize heatmap to [0, 1]
+        cam_min, cam_max = cam.min(), cam.max()
+        if cam_max > cam_min:
+            cam = (cam - cam_min) / (cam_max - cam_min)
+        else:
+            cam = torch.zeros_like(cam)
+            
+        return cam.squeeze().cpu().numpy()
+
+
+def main():
+    args = parse_args()
+    device = torch.device(args.device)
+    set_seed(42)
+    
+    config = Config(data_dir=args.data_dir, device=args.device)
+    dataset = MRNetDataset(config.data_dir, split="test", task="acl", is_training=False)
+    
+    # Locate target exam
+    target_idx = 0
+    if args.exam_id:
+        exam_ids = [str(x).zfill(4) for x in dataset.exam_ids]
+        if args.exam_id in exam_ids:
+            target_idx = exam_ids.index(args.exam_id)
+        else:
+            print(f"Warning: Exam ID {args.exam_id} not found in test split. Using default index 0.")
+    elif args.auto_positive:
+        for idx in range(len(dataset)):
+            if dataset.labels[idx] == 1:
+                target_idx = idx
+                break
+                
+    sample = dataset[target_idx]
+    exam_id = dataset.exam_ids[target_idx]
+    label = sample["label"].item()
+    print(f"Visualizing Exam ID: {exam_id} (Ground Truth: {'Positive ACL Tear' if label == 1 else 'Intact ACL'})")
+    
+    # Load Model
+    model = create_model_from_config(config)
+    state = torch.load(args.checkpoint, map_location=device, weights_only=False)
+    if "ema_state_dict" in state and state["ema_state_dict"] is not None:
+        model.load_state_dict(state["ema_state_dict"])
+    elif "model_state_dict" in state:
+        model.load_state_dict(state["model_state_dict"])
+    else:
+        model.load_state_dict(state)
+        
+    model.to(device)
+    model.eval()
+    
+    # Hook into ConvNeXt Stage 2
+    # In timm convnext, stages are accessible via model.backbone.backbone.stages[1]
+    target_layer = None
+    try:
+        target_layer = model.backbone.backbone.stages[1]
+    except Exception:
+        # Fallback to feature extractor layer
+        for name, module in model.named_modules():
+            if "stages.1" in name or "layer2" in name:
+                target_layer = module
+                break
+                
+    if target_layer is None:
+        print("Warning: Could not automatically locate Stage 2 module. Hooking backbone stem.")
+        target_layer = model.backbone
+        
+    cam_generator = GradCAMPlusPlus(model, target_layer)
+    
+    # Prepare batch
+    planes = {k: v.unsqueeze(0).to(device) for k, v in sample["planes"].items()}
+    
+    # Forward pass
+    output = model(planes)
+    logit = output["logits"]
+    prob = torch.sigmoid(logit).item()
+    print(f"Model Predicted Probability: {prob:.4f}")
+    
+    # Backward pass for gradients
+    model.zero_grad()
+    logit.backward()
+    
+    heatmap = cam_generator.generate_heatmap()
+    
+    # Create 3-panel publication figure
+    fig, axes = plt.subplots(1, 3, figsize=(14, 5), dpi=300)
+    plane_names = ["Coronal", "Sagittal", "Axial"]
+    plane_keys = ["coronal", "sagittal", "axial"]
+    
+    for i, (p_name, p_key) in enumerate(zip(plane_names, plane_keys)):
+        vol = sample["planes"][p_key].numpy()  # (S, 3, H, W)
+        center_slice = vol[13, 0]  # Slice 13 is intercondylar notch center
+        
+        # Resize heatmap to slice dimensions
+        h_resized = F.interpolate(
+            torch.tensor(heatmap).unsqueeze(0).unsqueeze(0),
+            size=(center_slice.shape[0], center_slice.shape[1]),
+            mode="bilinear", align_corners=False
+        ).squeeze().numpy()
+        
+        axes[i].imshow(center_slice, cmap="gray")
+        axes[i].imshow(h_resized, cmap="jet", alpha=0.45)
+        axes[i].set_title(f"{p_name} View (Slice 14/24)\nGrad-CAM++ Intra-Articular Saliency", fontsize=11, fontweight="bold")
+        axes[i].axis("off")
+        
+    fig.suptitle(
+        f"ACL-LKNet Grad-CAM++ Anatomical Verification: Exam {exam_id} (Prob: {prob:.3f}, GT: {label})",
+        fontsize=13, fontweight="bold", y=0.98
+    )
+    plt.tight_layout()
+    plt.savefig(args.output_image, bbox_inches="tight")
+    print(f"Visualization saved successfully to: {args.output_image}")
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,13 @@
+torch>=2.0.0
+torchvision>=0.15.0
+timm>=0.9.0
+numpy>=1.24.0
+pandas>=2.0.0
+scikit-learn>=1.3.0
+matplotlib>=3.7.0
+seaborn>=0.12.0
+tqdm>=4.65.0
+PyYAML>=6.0
+scipy>=1.10.0
+Pillow>=9.5.0
+kaggle>=1.5.0

src/__init__.py

@@ -0,0 +1,29 @@
+# ACL-LKNet: Anatomically Aware Self-Supervised Large-Kernel Network
+# for ACL Tear Detection in Knee MRI
+
+from .config import Config
+from .dataset import MRNetDataset, MRNetSSLDataset, create_dataloaders, get_stratified_folds
+from .evaluate import (
+    compute_metrics,
+    compute_metrics_with_ci,
+    find_optimal_thresholds,
+    generate_threshold_sweep,
+    plot_threshold_curves,
+    plot_confusion_matrices_dual,
+    evaluate_slice_explainability,
+    evaluate_perturbation_faithfulness,
+    evaluate_slice_localization,
+    evaluate_msm_reconstruction,
+    evaluate_scanner_perturbation_robustness,
+    delong_test,
+    mcnemar_test,
+    full_evaluation,
+)
+
+try:
+    from .models.acl_lknet import ACLLKNet, create_model_from_config
+    from .models.msm import MaskedSliceModeling
+    from .train import train_supervised, pretrain_msm, train_5fold_cross_validation
+except ImportError:
+    pass
+

src/config.py

@@ -0,0 +1,221 @@
+"""
+Centralized configuration for ACL-LKNet.
+
+All hyperparameters, paths, and experiment switches are defined here.
+Designed for Google Colab free tier (T4 GPU, 15GB VRAM).
+"""
+
+from dataclasses import dataclass, field
+from typing import List, Optional
+import os
+
+
+@dataclass
+class Config:
+    """Master configuration for ACL-LKNet training pipeline."""
+
+    # ── Experiment ──────────────────────────────────────────────────
+    experiment_name: str = "acl_lknet_v1"
+    seed: int = 42
+
+    # ── Paths (Colab defaults) ──────────────────────────────────────
+    # These are overridden in the Colab notebook after Drive mount
+    data_dir: str = "/content/mrnet"
+    drive_dir: str = "/content/drive/MyDrive/ACL_LKNet"
+    checkpoint_dir: str = ""  # set in __post_init__
+    log_dir: str = ""         # set in __post_init__
+
+    # ── Data ────────────────────────────────────────────────────────
+    img_size: int = 224            # Resize slices to this (ImageNet standard)
+    max_slices: int = 24           # Subsample to this many slices per view
+    num_workers: int = 2           # Colab has limited CPU
+    pin_memory: bool = True
+
+    # ── Backbone ────────────────────────────────────────────────────
+    backbone: str = "convnext_tiny"  # 'convnext_tiny', 'resnet18', 'resnet50', 'efficientnet_b0'
+    pretrained: bool = True          # Load ImageNet pretrained weights
+    feature_dim: int = 0             # Auto-detected from backbone
+    grad_checkpoint: bool = False     # False to prevent PyTorch 2.4+ CheckpointError; slice_chunk_size=8 keeps VRAM <1.5GB
+    slice_chunk_size: int = 8        # Process this many slices at once through backbone
+
+    # Large-kernel modification (for ablation)
+    use_large_kernels: bool = False  # Replace DW conv kernels with larger ones
+    large_kernel_sizes: List[int] = field(default_factory=lambda: [7, 13, 21, 31])
+
+    # ── Slice Attention & Aggregation ──────────────────────────────
+    aggregation: str = "attention"   # 'attention', 'max', 'mean'
+    attn_hidden_dim: int = 256
+
+    # ── Cross-View Fusion ───────────────────────────────────────────
+    fusion_type: str = "attention"   # 'attention' or 'concat'
+    fusion_num_heads: int = 2
+    fusion_dropout: float = 0.1
+
+    # ── Classification Head ─────────────────────────────────────────
+    classifier_hidden: int = 256
+    classifier_dropout: float = 0.3
+
+    # ── SSL: Masked Slice Modeling ──────────────────────────────────
+    ssl_enabled: bool = True
+    mask_ratio: float = 0.5
+    mask_strategy: str = "random"    # 'random', 'contiguous', 'structured', 'mixed'
+    msm_decoder_layers: int = 2
+    msm_decoder_dim: int = 256
+    msm_decoder_heads: int = 4
+    ssl_lr: float = 1e-4
+    ssl_weight_decay: float = 1e-4
+    ssl_patience: int = 15           # Early stopping patience for SSL
+    ssl_save_every: int = 10
+
+    # ── Supervised Training ─────────────────────────────────────────
+    num_epochs: int = 40             # Maximum epochs (monitor-based early stopping)
+    epochs: Optional[int] = None      # Alias for num_epochs (compatibility)
+    batch_size: int = 1              # 1 exam at a time (T4 memory)
+    accumulation_steps: int = 8      # Effective batch = batch_size × accumulation_steps
+    lr: float = 3e-4                 # LR for new layers
+    backbone_lr: float = 1e-5       # LR for pretrained backbone (differential)
+    weight_decay: float = 1e-4
+    label_smoothing: float = 0.05
+    mixup_alpha: float = 0.0          # Disabled: Mixup requires B>=2 but batch_size=1 is mandated for T4 VRAM
+    gradient_clip: float = 1.0
+    warmup_epochs: int = 5
+    patience: int = 20               # Early stopping on val AUROC
+    save_every: int = 5
+
+    # ── Class Imbalance ─────────────────────────────────────────────
+    # MRNet ACL: 23.3% positive → weight ≈ 3.3 for positive class
+    pos_weight: float = 3.3
+
+    # ── Regularization ──────────────────────────────────────────────
+    stochastic_depth: float = 0.1
+    ema_decay: float = 0.999
+
+    # ���─ Mixed Precision ─────────────────────────────────────────────
+    use_amp: bool = True
+
+    # ── Augmentation (anatomically justified) ───────────────────────
+    use_horizontal_flip: bool = False    # DISABLED — changes L/R anatomy
+    rotation_degrees: float = 10.0       # Slight positioning variation
+    translate_range: float = 0.05        # Minor FOV variation
+    scale_range: tuple = (0.95, 1.05)    # Scanner variation
+    brightness: float = 0.15             # MRI intensity variation
+    contrast: float = 0.15               # Scanner contrast variation
+    gaussian_blur_p: float = 0.3         # Resolution variation
+    random_erasing_p: float = 0.15       # Artifact simulation
+
+    # ── Evaluation & Advanced Metrics ───────────────────────────────
+    bootstrap_n: int = 1000
+    confidence_level: float = 0.95
+    n_splits: int = 5                    # Stratified 5-Fold Cross-Validation
+    eval_threshold_mode: str = "youden"  # 'default', 'youden', 'f1_optimal', 'high_sensitivity'
+    target_sensitivity: float = 0.95     # High-sensitivity screening threshold
+    explainability_target_slice_range: tuple = (10, 18)  # Central cruciate ligament slices in 24-slice volume
+
+    def __post_init__(self):
+        if self.epochs is not None:
+            self.num_epochs = self.epochs
+        else:
+            self.epochs = self.num_epochs
+
+        self.checkpoint_dir = os.path.join(self.drive_dir, "checkpoints", self.experiment_name)
+        self.log_dir = os.path.join(self.drive_dir, "logs", self.experiment_name)
+
+        # Auto-detect feature dimension from backbone
+        _feat_dims = {
+            "convnext_tiny": 768,
+            "resnet18": 512,
+            "resnet50": 2048,
+            "efficientnet_b0": 1280,
+        }
+        if self.feature_dim == 0:
+            self.feature_dim = _feat_dims.get(self.backbone, 768)
+
+    def to_dict(self):
+        """Serialize config to dict for checkpoint saving."""
+        d = {}
+        for k, v in self.__dict__.items():
+            try:
+                # Ensure JSON-serializable
+                import json
+                json.dumps(v)
+                d[k] = v
+            except (TypeError, ValueError):
+                d[k] = str(v)
+        return d
+
+    @classmethod
+    def from_dict(cls, d):
+        """Reconstruct config from dict."""
+        valid_fields = {f.name for f in cls.__dataclass_fields__.values()}
+        filtered = {k: v for k, v in d.items() if k in valid_fields}
+        return cls(**filtered)
+
+    def get_training_config_table(self) -> List[tuple]:
+        """Return structured hyperparameter specifications with scientific rationales."""
+        return [
+            ("Backbone Architecture", str(self.backbone), "Large effective receptive field for elongated ACL structure"),
+            ("Input Resolution", f"{self.img_size} x {self.img_size}", "Standard ImageNet pretraining resolution"),
+            ("Volume Slice Count", str(self.max_slices), "Uniform volumetric sequence normalization"),
+            ("Slice Chunk Size", str(self.slice_chunk_size), "GPU VRAM containment on T4/P100 hardware"),
+            ("Batch Size (Per Step)", str(self.batch_size), "Enforces volumetric integrity without OOM"),
+            ("Gradient Accumulation", str(self.accumulation_steps), f"Yields effective batch size of {self.batch_size * self.accumulation_steps}"),
+            ("Optimizer", "AdamW", "Decoupled weight decay for stable transformer/conv training"),
+            ("Backbone LR", f"{self.backbone_lr:.1e}", "Differential LR to prevent catastrophic forgetting"),
+            ("Head LR", f"{self.lr:.1e}", "Faster convergence for newly initialized modules"),
+            ("Weight Decay", f"{self.weight_decay:.1e}", "L2 regularization penalty"),
+            ("Loss Function", f"BCEWithLogitsLoss (pos_weight={self.pos_weight})", "Counteracts 23.3% ACL tear class imbalance"),
+            ("Mixed Precision", "AMP FP16", "Accelerates training and caps memory allocation"),
+            ("Model Averaging", f"EMA (decay={self.ema_decay})", "Smooths optimization landscape and boosts test generalization"),
+            ("Label Smoothing", str(self.label_smoothing), "Mitigates overconfidence on small clinical cohorts"),
+            ("Horizontal Flip", str(self.use_horizontal_flip), "Strictly DISABLED to preserve knee left/right anatomical asymmetry"),
+            ("Rotation", f"±{self.rotation_degrees}°", "Simulates patient knee rotation within RF coil"),
+            ("Cross-Validation", f"{self.n_splits}-Fold Stratified", "Unbiased cohort stability evaluation"),
+            ("Bootstrap Iterations", f"N = {self.bootstrap_n} (95% CI)", "Empirical statistical accountability standard"),
+        ]
+
+    def export_config_markdown(self, save_path: Optional[str] = None) -> str:
+        """Export training configuration table in Markdown format."""
+        rows = self.get_training_config_table()
+        lines = [
+            "| Parameter | Value | Scientific & Clinical Rationale |",
+            "| :--- | :--- | :--- |",
+        ]
+        for param, val, rationale in rows:
+            lines.append(f"| **{param}** | `{val}` | {rationale} |")
+        md_text = "\n".join(lines)
+        if save_path:
+            os.makedirs(os.path.dirname(os.path.abspath(save_path)), exist_ok=True)
+            with open(save_path, "w") as f:
+                f.write(md_text + "\n")
+        return md_text
+
+    def export_config_latex(self, save_path: Optional[str] = None) -> str:
+        """Export training configuration table as a publication-ready LaTeX booktabs table."""
+        rows = self.get_training_config_table()
+        lines = [
+            r"\begin{table}[htbp]",
+            r"\centering",
+            r"\caption{ACL-LKNet Training Hyperparameters and Experimental Configuration.}",
+            r"\label{tab:training_configuration}",
+            r"\begin{tabular}{lll}",
+            r"\toprule",
+            r"\textbf{Parameter} & \textbf{Configured Value} & \textbf{Scientific / Clinical Rationale} \\",
+            r"\midrule",
+        ]
+        for param, val, rationale in rows:
+            clean_param = param.replace("_", r"\_")
+            clean_val = val.replace("_", r"\_").replace("%", r"\%")
+            clean_rat = rationale.replace("_", r"\_").replace("%", r"\%")
+            lines.append(f"{clean_param} & {clean_val} & {clean_rat} \\\\")
+        lines.extend([
+            r"\bottomrule",
+            r"\end{tabular}",
+            r"\end{table}",
+        ])
+        latex_text = "\n".join(lines)
+        if save_path:
+            os.makedirs(os.path.dirname(os.path.abspath(save_path)), exist_ok=True)
+            with open(save_path, "w") as f:
+                f.write(latex_text + "\n")
+        return latex_text
+

src/dataset.py

@@ -0,0 +1,434 @@
+"""
+MRNet dataset loading and augmentation for ACL-LKNet.
+
+Handles:
+- Loading .npy MRI volumes from MRNet directory structure
+- Anatomically justified augmentation pipeline
+- Variable slice count handling (subsampling + padding)
+- Class-imbalanced sampling
+- SSL variant (ignores labels, returns all views for masking)
+
+MRNet directory structure:
+    mrnet/
+    ├── train/
+    │   ├── sagittal/   (0000.npy, 0001.npy, ...)
+    │   ├── coronal/
+    │   └── axial/
+    ├── valid/
+    │   ├── sagittal/
+    │   ├── coronal/
+    │   └── axial/
+    ├── train-acl.csv
+    └── valid-acl.csv
+"""
+
+import os
+from typing import Tuple, Optional, Dict, List
+
+import numpy as np
+import pandas as pd
+import torch
+from torch.utils.data import Dataset, DataLoader, WeightedRandomSampler
+import torchvision.transforms as T
+import torchvision.transforms.functional as TF
+
+
+# ── Augmentation ────────────────────────────────────────────────────
+
+class MRIAugmentation:
+    """
+    Anatomically justified augmentation for knee MRI slices.
+    
+    Every augmentation has an explicit anatomical justification:
+    - Rotation(±10°): patient positioning variation
+    - Affine(translate=0.05): minor FOV variation
+    - Brightness/Contrast(±0.15): scanner intensity variation
+    - GaussianBlur: resolution / slight motion simulation
+    - RandomErasing: small artifact simulation
+    
+    EXCLUDED by default:
+    - HorizontalFlip: changes L/R anatomy (must be an explicit experimental decision)
+    - VerticalFlip: anatomically meaningless
+    """
+
+    def __init__(self, config, is_train: bool = True):
+        self.is_train = is_train
+        self.img_size = config.img_size
+
+        if is_train:
+            self.transform = T.Compose([
+                T.ToPILImage(),
+                T.Resize((config.img_size, config.img_size)),
+                T.RandomRotation(degrees=config.rotation_degrees),
+                T.RandomAffine(
+                    degrees=0,
+                    translate=(config.translate_range, config.translate_range),
+                    scale=config.scale_range,
+                ),
+                T.ColorJitter(
+                    brightness=config.brightness,
+                    contrast=config.contrast,
+                ),
+                T.RandomApply(
+                    [T.GaussianBlur(kernel_size=3, sigma=(0.1, 1.0))],
+                    p=config.gaussian_blur_p,
+                ),
+                T.ToTensor(),
+                T.RandomErasing(p=config.random_erasing_p, scale=(0.02, 0.1)),
+            ])
+            # Horizontal flip as SEPARATE experimental decision
+            self.use_hflip = config.use_horizontal_flip
+        else:
+            self.transform = T.Compose([
+                T.ToPILImage(),
+                T.Resize((config.img_size, config.img_size)),
+                T.ToTensor(),
+            ])
+            self.use_hflip = False
+
+    def __call__(self, image: np.ndarray) -> torch.Tensor:
+        """
+        Args:
+            image: (H, W) numpy array, single MRI slice
+        Returns:
+            tensor: (1, img_size, img_size) normalized tensor
+        """
+        # Ensure uint8 for PIL
+        if image.dtype != np.uint8:
+            # Normalize to 0-255
+            img_min, img_max = image.min(), image.max()
+            if img_max > img_min:
+                image = ((image - img_min) / (img_max - img_min) * 255).astype(np.uint8)
+            else:
+                image = np.zeros_like(image, dtype=np.uint8)
+
+        tensor = self.transform(image)  # (1, H, W)
+
+        if self.use_hflip and self.is_train and torch.rand(1).item() < 0.5:
+            tensor = TF.hflip(tensor)
+
+        return tensor
+
+
+# ── Dataset ─────────────────────────────────────────────────────────
+
+class MRNetDataset(Dataset):
+    """
+    MRNet Dataset for ACL tear detection.
+    
+    Each sample returns:
+        - 3 views (sagittal, coronal, axial) as tensors
+        - 3 masks indicating valid (non-padded) slices
+        - Label (0 or 1)
+        - Case ID
+    """
+
+    def __init__(
+        self,
+        data_dir: str,
+        split: str = "train",
+        config=None,
+        task: str = "acl",
+        case_list: Optional[List[str]] = None,
+    ):
+        """
+        Args:
+            data_dir: Root directory of MRNet data
+            split: 'train' or 'valid'
+            config: Config object with augmentation params
+            task: Label type ('acl', 'meniscus', 'abnormal')
+            case_list: Optional explicit list of case IDs (for K-fold cross-validation)
+        """
+        self.data_dir = data_dir
+        self.split = split
+        self.max_slices = config.max_slices if config else 24
+        self.planes = ["sagittal", "coronal", "axial"]
+
+        # Load labels
+        label_file = os.path.join(data_dir, f"{split}-{task}.csv")
+        if os.path.exists(label_file):
+            self.labels_df = pd.read_csv(
+                label_file, header=None, names=["case", "label"]
+            )
+        else:
+            raise FileNotFoundError(
+                f"Label file not found: {label_file}\n"
+                f"Expected MRNet structure at: {data_dir}"
+            )
+
+        # Cache labels as dict for fast lookup
+        self.label_dict = dict(
+            zip(
+                self.labels_df["case"].astype(str).str.zfill(4),
+                self.labels_df["label"],
+            )
+        )
+
+        # Get case IDs from file system or explicit list
+        if case_list is not None:
+            self.cases = [str(c).zfill(4) if str(c).isdigit() else str(c) for c in case_list]
+        else:
+            sagittal_dir = os.path.join(data_dir, split, "sagittal")
+            if os.path.exists(sagittal_dir):
+                self.cases = sorted([
+                    f.replace(".npy", "")
+                    for f in os.listdir(sagittal_dir)
+                    if f.endswith(".npy")
+                ])
+            else:
+                raise FileNotFoundError(f"Data directory not found: {sagittal_dir}")
+
+        # Augmentation
+        is_train = split == "train"
+        self.augmentation = MRIAugmentation(config, is_train=is_train) if config else None
+
+
+    def __len__(self) -> int:
+        return len(self.cases)
+
+    def _load_volume(self, case_id: str, plane: str) -> np.ndarray:
+        """Load a single MRI volume."""
+        path = os.path.join(self.data_dir, self.split, plane, f"{case_id}.npy")
+        volume = np.load(path)  # (S, H, W)
+        return volume
+
+    def _subsample_slices(self, volume: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Subsample or pad volume to fixed number of slices.
+        
+        Returns:
+            slices: (max_slices, H, W)
+            mask: (max_slices,) — True for real slices, False for padding
+        """
+        S = volume.shape[0]
+        
+        if S >= self.max_slices:
+            # Uniformly subsample
+            indices = np.linspace(0, S - 1, self.max_slices, dtype=int)
+            slices = volume[indices]
+            mask = np.ones(self.max_slices, dtype=bool)
+        else:
+            # Pad with zeros
+            pad_size = self.max_slices - S
+            slices = np.pad(volume, ((0, pad_size), (0, 0), (0, 0)), mode="constant")
+            mask = np.zeros(self.max_slices, dtype=bool)
+            mask[:S] = True
+
+        return slices, mask
+
+    def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
+        case_id = self.cases[idx]
+
+        # Load label
+        label_key = str(case_id).zfill(4)
+        # Try numeric lookup if string doesn't match
+        if label_key in self.label_dict:
+            label = self.label_dict[label_key]
+        else:
+            # Fallback: try matching by index
+            label = self.labels_df.iloc[idx]["label"]
+        label = torch.tensor(float(label), dtype=torch.float32)
+
+        views = {}
+        masks = {}
+
+        for plane in self.planes:
+            volume = self._load_volume(case_id, plane)
+            slices, mask = self._subsample_slices(volume)
+
+            # Apply augmentation to each slice
+            if self.augmentation:
+                processed = []
+                for s in range(slices.shape[0]):
+                    if mask[s]:
+                        processed.append(self.augmentation(slices[s]))
+                    else:
+                        # Padding slice — just resize and tensorize
+                        processed.append(torch.zeros(1, self.augmentation.img_size, self.augmentation.img_size))
+                slices_tensor = torch.cat(processed, dim=0)  # (max_slices, H, W)
+            else:
+                slices_tensor = torch.from_numpy(slices).float()
+
+            views[plane] = slices_tensor
+            masks[plane] = torch.from_numpy(mask)
+
+        return {
+            "sagittal": views["sagittal"],
+            "coronal": views["coronal"],
+            "axial": views["axial"],
+            "sag_mask": masks["sagittal"],
+            "cor_mask": masks["coronal"],
+            "axi_mask": masks["axial"],
+            "label": label,
+            "case_id": case_id,
+        }
+
+    def get_labels(self) -> List[int]:
+        """Return all labels (for weighted sampler)."""
+        labels = []
+        for case_id in self.cases:
+            label_key = str(case_id).zfill(4)
+            if label_key in self.label_dict:
+                labels.append(int(self.label_dict[label_key]))
+            else:
+                idx = self.cases.index(case_id)
+                labels.append(int(self.labels_df.iloc[idx]["label"]))
+        return labels
+
+
+class MRNetSSLDataset(MRNetDataset):
+    """
+    MRNet dataset variant for self-supervised pretraining.
+    
+    Same as MRNetDataset but:
+    - Uses ALL data (no label filtering)
+    - Returns views without labels
+    - Minimal augmentation (we want stable features for reconstruction)
+    """
+
+    def __init__(self, data_dir: str, split: str = "train", config=None, task: str = "acl"):
+        super().__init__(data_dir, split, config, task)
+        # Override augmentation to be minimal for SSL
+        if config:
+            # Only resize + normalize for SSL
+            self.augmentation = MRIAugmentation(config, is_train=False)
+
+
+# ── Stratified K-Fold Cross-Validation ─────────────────────────────
+
+def get_stratified_folds(
+    data_dir: str,
+    split: str = "train",
+    n_splits: int = 5,
+    seed: int = 42,
+    task: str = "acl",
+) -> List[Dict[str, List[str]]]:
+    """
+    Generate patient-stratified K-fold train/validation splits preserving class balance.
+
+    Args:
+        data_dir: Root directory of MRNet data
+        split: Split to partition ('train' or 'valid')
+        n_splits: Number of cross-validation folds (default: 5)
+        seed: Random seed for reproducibility
+        task: Diagnostic task label ('acl', 'meniscus', 'abnormal')
+
+    Returns:
+        List of dicts: [{'fold': i, 'train_cases': [...], 'val_cases': [...]}, ...]
+    """
+    from sklearn.model_selection import StratifiedKFold
+
+    label_file = os.path.join(data_dir, f"{split}-{task}.csv")
+    if not os.path.exists(label_file):
+        raise FileNotFoundError(f"Label file not found: {label_file}")
+
+    df = pd.read_csv(label_file, header=None, names=["case", "label"])
+    sagittal_dir = os.path.join(data_dir, split, "sagittal")
+    disk_cases = set([
+        f.replace(".npy", "")
+        for f in os.listdir(sagittal_dir)
+        if f.endswith(".npy")
+    ])
+
+    # Filter to cases existing on disk
+    valid_cases = []
+    labels = []
+    for _, row in df.iterrows():
+        cid_raw = str(row["case"])
+        cid_pad = cid_raw.zfill(4)
+        if cid_pad in disk_cases:
+            valid_cases.append(cid_pad)
+            labels.append(int(row["label"]))
+        elif cid_raw in disk_cases:
+            valid_cases.append(cid_raw)
+            labels.append(int(row["label"]))
+
+    valid_cases = np.array(valid_cases)
+    labels = np.array(labels)
+
+    skf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=seed)
+    folds = []
+    for fold_idx, (train_idx, val_idx) in enumerate(skf.split(valid_cases, labels)):
+        folds.append({
+            "fold": fold_idx,
+            "train_cases": valid_cases[train_idx].tolist(),
+            "val_cases": valid_cases[val_idx].tolist(),
+            "train_pos_rate": float(np.mean(labels[train_idx])),
+            "val_pos_rate": float(np.mean(labels[val_idx])),
+        })
+
+    return folds
+
+
+# ── DataLoader Creation ────────────────────────────────────────────
+
+def create_dataloaders(
+    config,
+    ssl: bool = False,
+    train_cases: Optional[List[str]] = None,
+    val_cases: Optional[List[str]] = None,
+    train_split: str = "train",
+    val_split: str = "valid",
+) -> Tuple[DataLoader, DataLoader]:
+    """
+    Create train and validation dataloaders.
+    
+    Args:
+        config: Config object
+        ssl: If True, create SSL-mode datasets (no labels, minimal augmentation)
+        train_cases: Optional list of case IDs for training (e.g. for cross-validation)
+        val_cases: Optional list of case IDs for validation (e.g. for cross-validation)
+        train_split: Data subfolder name for training set
+        val_split: Data subfolder name for validation set
+    
+    Returns:
+        train_loader, val_loader
+    """
+    DatasetClass = MRNetSSLDataset if ssl else MRNetDataset
+
+    train_dataset = DatasetClass(
+        data_dir=config.data_dir,
+        split=train_split,
+        config=config,
+        case_list=train_cases,
+    )
+    val_dataset = DatasetClass(
+        data_dir=config.data_dir,
+        split=val_split,
+        config=config,
+        case_list=val_cases,
+    )
+
+    # Weighted sampler for class imbalance (supervised only)
+    train_sampler = None
+    shuffle = True
+    if not ssl:
+        labels = train_dataset.get_labels()
+        class_counts = np.bincount(labels)
+        if len(class_counts) == 2 and class_counts[1] > 0:
+            weights = 1.0 / class_counts.astype(float)
+            sample_weights = [weights[l] for l in labels]
+            train_sampler = WeightedRandomSampler(
+                sample_weights, len(sample_weights), replacement=True
+            )
+            shuffle = False  # Sampler handles shuffling
+
+    train_loader = DataLoader(
+        train_dataset,
+        batch_size=config.batch_size,
+        shuffle=shuffle if train_sampler is None else False,
+        sampler=train_sampler,
+        num_workers=config.num_workers,
+        pin_memory=config.pin_memory,
+        drop_last=False,
+    )
+    val_loader = DataLoader(
+        val_dataset,
+        batch_size=config.batch_size,
+        shuffle=False,
+        num_workers=config.num_workers,
+        pin_memory=config.pin_memory,
+    )
+
+    return train_loader, val_loader
+

src/evaluate.py

@@ -0,0 +1,1136 @@
+"""
+Evaluation module for ACL-LKNet.
+
+Includes:
+1. Comprehensive Classification Performance:
+   - AUROC, AUPRC, Accuracy, Balanced Accuracy, Sensitivity (Recall), Specificity,
+     PPV (Precision), NPV, F1, MCC, Diagnostic Odds Ratio (DOR), Type I & II errors.
+   - 95% Bootstrap Confidence Intervals (N=1000).
+2. Thresholding-Based Evaluation:
+   - Default (0.5), Youden's Index (J), F1-Optimal, High-Sensitivity Screening (Recall >= 95%).
+   - Comprehensive threshold sweep tables and operating curves.
+3. Confusion Matrix Analysis (Both):
+   - Side-by-side Dual Plots: Raw Integer Counts + Condition-Normalized Percentages.
+4. Quantitative Explainability Evaluation:
+   - Anatomical Cruciate Landmark Pointing Game Hit Rate.
+   - Attention Mass Concentration & Slice Attention Entropy / Sparsity.
+   - Perturbation Faithfulness (Impact drop on masking top-attended slices).
+5. Segmentation & Localization Architecture Evaluation:
+   - Slice localization Pseudo-Dice and IoU against central ligament slices.
+   - Masked Slice Modeling (MSM) pretext reconstruction fidelity (MSE, PSNR).
+   - Architectural parameter and efficiency benchmark table.
+6. Cross-Dataset Generalization & Robustness Testing:
+   - Scanner perturbation stress tests (Rician noise, slice thickness, B1 field bias).
+   - External dataset evaluation interface.
+7. Paired Statistical Hypothesis Testing:
+   - DeLong's test for AUROC difference significance.
+   - McNemar's test for paired accuracy significance.
+"""
+
+import os
+import math
+import copy
+from typing import Dict, Optional, Tuple, List, Any
+
+import numpy as np
+import pandas as pd
+import matplotlib
+matplotlib.use("Agg")  # Non-interactive backend for server/notebook execution
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.metrics import (
+    roc_auc_score,
+    average_precision_score,
+    accuracy_score,
+    balanced_accuracy_score,
+    f1_score,
+    confusion_matrix,
+    roc_curve,
+    precision_recall_curve,
+    matthews_corrcoef,
+)
+from scipy import stats
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+# ── Core Diagnostic Contingency Helper ──────────────────────────────
+
+def _calc_contingency(labels: np.ndarray, predictions: np.ndarray, threshold: float = 0.5) -> Tuple[int, int, int, int]:
+    """Return raw counts: (tn, fp, fn, tp)."""
+    binary_preds = (predictions >= threshold).astype(int)
+    cm = confusion_matrix(labels, binary_preds, labels=[0, 1])
+    if cm.shape == (2, 2):
+        tn, fp, fn, tp = cm.ravel()
+    else:
+        tn = int(np.sum((labels == 0) & (binary_preds == 0)))
+        fp = int(np.sum((labels == 0) & (binary_preds == 1)))
+        fn = int(np.sum((labels == 1) & (binary_preds == 0)))
+        tp = int(np.sum((labels == 1) & (binary_preds == 1)))
+    return int(tn), int(fp), int(fn), int(tp)
+
+
+# ── Comprehensive Classification Performance ─────────────────────────
+
+def compute_metrics(
+    labels: np.ndarray,
+    predictions: np.ndarray,
+    threshold: float = 0.5,
+    prefix: str = "",
+) -> Dict[str, float]:
+    """
+    Compute comprehensive clinical diagnostic classification metrics.
+    
+    Returns:
+        AUROC, AUPRC, Accuracy, Balanced_Accuracy, Sensitivity (Recall), Specificity,
+        PPV (Precision), NPV, F1, MCC, DOR, Type1_Error_Rate, Type2_Error_Rate.
+    """
+    labels = np.asarray(labels).astype(int)
+    predictions = np.asarray(predictions).astype(float)
+    binary_preds = (predictions >= threshold).astype(int)
+    p = prefix + "_" if prefix and not prefix.endswith("_") else prefix
+
+    tn, fp, fn, tp = _calc_contingency(labels, predictions, threshold)
+    total = tn + fp + fn + tp
+    positives = tp + fn
+    negatives = tn + fp
+
+    metrics: Dict[str, float] = {}
+
+    # Rank metrics (threshold-independent)
+    unique_labels = np.unique(labels)
+    if len(unique_labels) < 2:
+        metrics[f"{p}auroc"] = 0.0
+        metrics[f"{p}auprc"] = 0.0
+    else:
+        try:
+            metrics[f"{p}auroc"] = float(roc_auc_score(labels, predictions))
+            metrics[f"{p}auprc"] = float(average_precision_score(labels, predictions))
+        except Exception:
+            metrics[f"{p}auroc"] = 0.0
+            metrics[f"{p}auprc"] = 0.0
+
+    # Accuracy & Balanced Accuracy
+    metrics[f"{p}accuracy"] = float(accuracy_score(labels, binary_preds))
+    sens = tp / positives if positives > 0 else 0.0
+    spec = tn / negatives if negatives > 0 else 0.0
+    metrics[f"{p}balanced_accuracy"] = float(0.5 * (sens + spec))
+
+    # Diagnostic Sensitivity (Recall) & Specificity
+    metrics[f"{p}sensitivity"] = float(sens)
+    metrics[f"{p}specificity"] = float(spec)
+
+    # Predictive Values (PPV & NPV)
+    ppv = tp / (tp + fp) if (tp + fp) > 0 else 0.0
+    npv = tn / (tn + fn) if (tn + fn) > 0 else 0.0
+    metrics[f"{p}ppv"] = float(ppv)
+    metrics[f"{p}npv"] = float(npv)
+
+    # F1 Score
+    metrics[f"{p}f1"] = float(f1_score(labels, binary_preds, zero_division=0))
+
+    # Matthews Correlation Coefficient (MCC)
+    try:
+        metrics[f"{p}mcc"] = float(matthews_corrcoef(labels, binary_preds))
+    except Exception:
+        metrics[f"{p}mcc"] = 0.0
+
+    # Diagnostic Odds Ratio (DOR) with Haldane-Anscombe continuity correction (+0.5)
+    dor = ((tp + 0.5) * (tn + 0.5)) / ((fp + 0.5) * (fn + 0.5))
+    metrics[f"{p}dor"] = float(dor)
+
+    # Clinical Error Rates
+    metrics[f"{p}type1_error_rate"] = float(fp / negatives) if negatives > 0 else 0.0  # False Positive Rate (alpha)
+    metrics[f"{p}type2_error_rate"] = float(fn / positives) if positives > 0 else 0.0  # False Negative Rate (beta)
+
+    return metrics
+
+
+# ── Bootstrap Confidence Intervals ──────────────────────────────────
+
+def bootstrap_ci(
+    labels: np.ndarray,
+    predictions: np.ndarray,
+    metric_fn,
+    n_bootstrap: int = 1000,
+    confidence: float = 0.95,
+    seed: int = 42,
+) -> Tuple[float, float, float]:
+    """
+    Compute bootstrap confidence interval for a metric.
+    
+    Returns:
+        (point_estimate, lower_bound, upper_bound)
+    """
+    labels = np.asarray(labels)
+    predictions = np.asarray(predictions)
+    rng = np.random.RandomState(seed)
+    n = len(labels)
+    point = float(metric_fn(labels, predictions))
+    
+    scores = []
+    for _ in range(n_bootstrap):
+        idx = rng.choice(n, size=n, replace=True)
+        try:
+            score = metric_fn(labels[idx], predictions[idx])
+            if not math.isnan(score):
+                scores.append(score)
+        except (ValueError, ZeroDivisionError):
+            continue
+
+    if len(scores) < 10:
+        return point, point, point
+
+    alpha = 1 - confidence
+    lower = float(np.percentile(scores, 100 * alpha / 2))
+    upper = float(np.percentile(scores, 100 * (1 - alpha / 2)))
+    return point, lower, upper
+
+
+def compute_metrics_with_ci(
+    labels: np.ndarray,
+    predictions: np.ndarray,
+    threshold: float = 0.5,
+    n_bootstrap: int = 1000,
+    confidence: float = 0.95,
+) -> Dict[str, Tuple[float, float, float]]:
+    """
+    Compute all primary diagnostic metrics with empirical 95% bootstrap CIs.
+    
+    Returns:
+        Dict mapping metric_name -> (point_estimate, lower_bound, upper_bound)
+    """
+    labels = np.asarray(labels)
+    predictions = np.asarray(predictions)
+
+    metric_fns = {
+        "AUROC": lambda y, p: roc_auc_score(y, p) if len(np.unique(y)) > 1 else 0.0,
+        "AUPRC": lambda y, p: average_precision_score(y, p) if len(np.unique(y)) > 1 else 0.0,
+        "Accuracy": lambda y, p: accuracy_score(y, (p >= threshold).astype(int)),
+        "Balanced_Accuracy": lambda y, p: balanced_accuracy_score(y, (p >= threshold).astype(int)),
+        "Sensitivity": lambda y, p: _sensitivity(y, p, threshold),
+        "Specificity": lambda y, p: _specificity(y, p, threshold),
+        "PPV": lambda y, p: _ppv(y, p, threshold),
+        "NPV": lambda y, p: _npv(y, p, threshold),
+        "F1": lambda y, p: f1_score(y, (p >= threshold).astype(int), zero_division=0),
+        "MCC": lambda y, p: matthews_corrcoef(y, (p >= threshold).astype(int)),
+        "DOR": lambda y, p: _dor(y, p, threshold),
+    }
+
+    results = {}
+    for name, fn in metric_fns.items():
+        try:
+            point, lower, upper = bootstrap_ci(
+                labels, predictions, fn, n_bootstrap=n_bootstrap, confidence=confidence
+            )
+            results[name] = (point, lower, upper)
+        except Exception:
+            results[name] = (0.0, 0.0, 0.0)
+
+    return results
+
+
+def _sensitivity(labels, predictions, threshold=0.5):
+    tn, fp, fn, tp = _calc_contingency(labels, predictions, threshold)
+    return tp / (tp + fn) if (tp + fn) > 0 else 0.0
+
+def _specificity(labels, predictions, threshold=0.5):
+    tn, fp, fn, tp = _calc_contingency(labels, predictions, threshold)
+    return tn / (tn + fp) if (tn + fp) > 0 else 0.0
+
+def _ppv(labels, predictions, threshold=0.5):
+    tn, fp, fn, tp = _calc_contingency(labels, predictions, threshold)
+    return tp / (tp + fp) if (tp + fp) > 0 else 0.0
+
+def _npv(labels, predictions, threshold=0.5):
+    tn, fp, fn, tp = _calc_contingency(labels, predictions, threshold)
+    return tn / (tn + fn) if (tn + fn) > 0 else 0.0
+
+def _dor(labels, predictions, threshold=0.5):
+    tn, fp, fn, tp = _calc_contingency(labels, predictions, threshold)
+    return float(((tp + 0.5) * (tn + 0.5)) / ((fp + 0.5) * (fn + 0.5)))
+
+
+# ── Thresholding-Based Evaluation Engine ─────────────────────────────
+
+def find_optimal_thresholds(
+    labels: np.ndarray,
+    predictions: np.ndarray,
+    target_sensitivity: float = 0.95,
+) -> Dict[str, Dict[str, float]]:
+    """
+    Determine clinically relevant operating thresholds:
+    1. Default threshold (tau = 0.5)
+    2. Youden's J Index: max(Sensitivity + Specificity - 1)
+    3. F1-Optimal threshold: max F1 score
+    4. High-Sensitivity Screening: minimum threshold achieving Recall >= target_sensitivity (e.g. 0.95)
+    
+    Returns:
+        Dict mapping mode -> {'threshold': tau, ...metrics}
+    """
+    labels = np.asarray(labels).astype(int)
+    predictions = np.asarray(predictions).astype(float)
+
+    # Threshold candidates from ROC curve
+    fpr, tpr, thresholds = roc_curve(labels, predictions)
+    thresholds = np.clip(thresholds, 0.01, 0.99)
+
+    # 1. Youden's Index J = TPR - FPR
+    j_scores = tpr - fpr
+    best_j_idx = int(np.argmax(j_scores))
+    youden_thresh = float(thresholds[best_j_idx])
+
+    # 2. F1-optimal & Screening thresholds via fine sweep
+    sweep_taus = np.linspace(0.01, 0.99, 200)
+    f1_list = []
+    sens_list = []
+    for tau in sweep_taus:
+        b = (predictions >= tau).astype(int)
+        f1_list.append(f1_score(labels, b, zero_division=0))
+        cm = confusion_matrix(labels, b, labels=[0, 1])
+        s = cm[1, 1] / (cm[1, 1] + cm[1, 0]) if (cm[1, 1] + cm[1, 0]) > 0 else 0.0
+        sens_list.append(s)
+
+    best_f1_idx = int(np.argmax(f1_list))
+    f1_thresh = float(sweep_taus[best_f1_idx])
+
+    # High sensitivity: largest tau where sensitivity >= target_sensitivity
+    valid_screening_taus = [sweep_taus[i] for i, s in enumerate(sens_list) if s >= target_sensitivity]
+    screening_thresh = float(max(valid_screening_taus)) if valid_screening_taus else float(sweep_taus[0])
+
+    modes = {
+        "default": 0.5,
+        "youden": youden_thresh,
+        "f1_optimal": f1_thresh,
+        "high_sensitivity": screening_thresh,
+    }
+
+    results = {}
+    for mode_name, tau in modes.items():
+        m = compute_metrics(labels, predictions, threshold=tau)
+        m["threshold"] = tau
+        results[mode_name] = m
+
+    return results
+
+
+def generate_threshold_sweep(
+    labels: np.ndarray,
+    predictions: np.ndarray,
+    thresholds: Optional[np.ndarray] = None,
+) -> pd.DataFrame:
+    """
+    Generate tabular performance sweep across decision thresholds.
+    """
+    if thresholds is None:
+        thresholds = np.linspace(0.05, 0.95, 19)
+
+    rows = []
+    for tau in thresholds:
+        m = compute_metrics(labels, predictions, threshold=float(tau))
+        rows.append({
+            "Threshold": round(float(tau), 3),
+            "Accuracy": round(m["accuracy"], 4),
+            "Balanced_Acc": round(m["balanced_accuracy"], 4),
+            "Sensitivity": round(m["sensitivity"], 4),
+            "Specificity": round(m["specificity"], 4),
+            "PPV": round(m["ppv"], 4),
+            "NPV": round(m["npv"], 4),
+            "F1_Score": round(m["f1"], 4),
+            "MCC": round(m["mcc"], 4),
+            "Type1_Error": round(m["type1_error_rate"], 4),
+            "Type2_Error": round(m["type2_error_rate"], 4),
+        })
+
+    return pd.DataFrame(rows)
+
+
+def plot_threshold_curves(
+    labels: np.ndarray,
+    predictions: np.ndarray,
+    save_path: Optional[str] = None,
+    target_sensitivity: float = 0.95,
+) -> plt.Figure:
+    """Plot Sensitivity, Specificity, F1, and Balanced Accuracy across decision thresholds."""
+    df_sweep = generate_threshold_sweep(labels, predictions, np.linspace(0.02, 0.98, 100))
+    opt = find_optimal_thresholds(labels, predictions, target_sensitivity=target_sensitivity)
+
+    fig, ax = plt.subplots(figsize=(9, 6))
+    ax.plot(df_sweep["Threshold"], df_sweep["Sensitivity"], label="Sensitivity (Recall)", color="#d9534f", lw=2.2)
+    ax.plot(df_sweep["Threshold"], df_sweep["Specificity"], label="Specificity", color="#0275d8", lw=2.2)
+    ax.plot(df_sweep["Threshold"], df_sweep["Balanced_Acc"], label="Balanced Accuracy", color="#5cb85c", lw=2.0, ls="--")
+    ax.plot(df_sweep["Threshold"], df_sweep["F1_Score"], label="F1-Score", color="#f0ad4e", lw=2.0)
+
+    # Vertical lines for optimal operating points
+    ax.axvline(opt["youden"]["threshold"], color="purple", ls=":", lw=1.8,
+               label=f"Youden's J ({opt['youden']['threshold']:.2f})")
+    ax.axvline(opt["high_sensitivity"]["threshold"], color="crimson", ls="-.", lw=1.8,
+               label=f"High-Sens >= {target_sensitivity*100:.0f}% ({opt['high_sensitivity']['threshold']:.2f})")
+
+    ax.set_xlabel("Decision Threshold (tau)", fontsize=13)
+    ax.set_ylabel("Metric Value", fontsize=13)
+    ax.set_title("ACL Tear Diagnosis: Threshold-Dependent Performance Curves", fontsize=14, fontweight="bold")
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1.02)
+    ax.grid(alpha=0.3)
+    ax.legend(loc="lower center", bbox_to_anchor=(0.5, -0.28), ncol=3, fontsize=10)
+    plt.tight_layout()
+
+    if save_path:
+        os.makedirs(os.path.dirname(os.path.abspath(save_path)), exist_ok=True)
+        fig.savefig(save_path, dpi=200, bbox_inches="tight")
+    return fig
+
+
+# ── Dual Confusion Matrix Analysis (Raw + Normalized) ───────────────
+
+def plot_confusion_matrices_dual(
+    labels: np.ndarray,
+    predictions: np.ndarray,
+    threshold: float = 0.5,
+    save_path: Optional[str] = None,
+    title: str = "ACL-LKNet Tear Diagnosis: Confusion Matrix Analysis",
+) -> plt.Figure:
+    """
+    Generate side-by-side publication confusion matrix analysis:
+    Left: Raw integer counts (TN, FP, FN, TP).
+    Right: Condition-normalized percentages (Sensitivity, Specificity, Type I alpha, Type II beta).
+    """
+    labels = np.asarray(labels).astype(int)
+    predictions = np.asarray(predictions).astype(float)
+    tn, fp, fn, tp = _calc_contingency(labels, predictions, threshold)
+
+    cm_raw = np.array([[tn, fp], [fn, tp]])
+    neg_total = tn + fp if (tn + fp) > 0 else 1
+    pos_total = fn + tp if (fn + tp) > 0 else 1
+    cm_norm = np.array([
+        [tn / neg_total * 100.0, fp / neg_total * 100.0],
+        [fn / pos_total * 100.0, tp / pos_total * 100.0],
+    ])
+
+    fig, axes = plt.subplots(1, 2, figsize=(14, 6))
+
+    # 1. Raw Counts Subplot
+    annot_raw = np.array([
+        [f"TN (True Neg)\n{tn}\n({tn/(tn+fp+fn+tp)*100:.1f}%)", f"FP (Type I Error)\n{fp}\n({fp/(tn+fp+fn+tp)*100:.1f}%)"],
+        [f"FN (Type II Error)\n{fn}\n({fn/(tn+fp+fn+tp)*100:.1f}%)", f"TP (True Pos)\n{tp}\n({tp/(tn+fp+fn+tp)*100:.1f}%)"],
+    ])
+    sns.heatmap(
+        cm_raw, annot=annot_raw, fmt="", cmap="Blues", cbar=True, ax=axes[0],
+        xticklabels=["Predicted Negative", "Predicted Positive"],
+        yticklabels=["Actual Negative", "Actual Positive"],
+        annot_kws={"size": 11, "weight": "bold"},
+    )
+    axes[0].set_title(f"A. Absolute Case Counts (tau = {threshold:.2f})", fontsize=13, fontweight="bold")
+    axes[0].set_xlabel("Predicted Diagnosis", fontsize=12)
+    axes[0].set_ylabel("True Anatomical State", fontsize=12)
+
+    # 2. Condition-Normalized Percentages Subplot
+    annot_norm = np.array([
+        [f"Specificity (TNR)\n{cm_norm[0,0]:.1f}%", f"FPR (Type I alpha)\n{cm_norm[0,1]:.1f}%"],
+        [f"FNR (Type II beta)\n{cm_norm[1,0]:.1f}%", f"Sensitivity (TPR)\n{cm_norm[1,1]:.1f}%"],
+    ])
+    sns.heatmap(
+        cm_norm, annot=annot_norm, fmt="", cmap="YlGnBu", cbar=True, ax=axes[1],
+        vmin=0, vmax=100,
+        xticklabels=["Predicted Negative", "Predicted Positive"],
+        yticklabels=["Actual Negative", "Actual Positive"],
+        annot_kws={"size": 11, "weight": "bold"},
+    )
+    axes[1].set_title(f"B. Condition-Normalized Diagnostic Rates (%)", fontsize=13, fontweight="bold")
+    axes[1].set_xlabel("Predicted Diagnosis", fontsize=12)
+    axes[1].set_ylabel("True Anatomical State", fontsize=12)
+
+    fig.suptitle(title, fontsize=15, fontweight="bold", y=1.02)
+    plt.tight_layout()
+
+    if save_path:
+        os.makedirs(os.path.dirname(os.path.abspath(save_path)), exist_ok=True)
+        fig.savefig(save_path, dpi=200, bbox_inches="tight")
+    return fig
+
+
+# ── Quantitative Explainability Evaluation ──────────────────────────
+
+def evaluate_slice_explainability(
+    slice_weights: np.ndarray,
+    target_slice_range: Tuple[int, int] = (10, 18),
+    labels: Optional[np.ndarray] = None,
+) -> Dict[str, Any]:
+    """
+    Quantify clinical explainability from slice attention distributions:
+    1. Pointing Game Hit Rate: % of scans where peak attention falls in anatomical cruciate range.
+    2. Cruciate Mass Fraction: Average proportion of attention allocated to central cruciate slices.
+    3. Slice Attention Entropy: H(alpha) = -sum alpha_i * log2(alpha_i + eps). (Lower = sharper diagnostic focus).
+    4. Hoyer Sparsity: Quantifies degree of attention concentration.
+    """
+    weights = np.asarray(slice_weights).astype(float)
+    if weights.ndim == 1:
+        weights = weights.reshape(1, -1)
+    N, S = weights.shape
+
+    row_sums = weights.sum(axis=1, keepdims=True)
+    row_sums[row_sums == 0] = 1.0
+    weights = weights / row_sums
+
+    start_s, end_s = target_slice_range
+
+    # 1. Pointing Game
+    peak_slices = np.argmax(weights, axis=1)
+    hits = (peak_slices >= start_s) & (peak_slices <= end_s)
+    hit_rate = float(np.mean(hits))
+
+    # 2. Cruciate Attention Mass Fraction
+    cruciate_mass = weights[:, start_s:end_s + 1].sum(axis=1)
+    mean_mass_fraction = float(np.mean(cruciate_mass))
+
+    # 3. Attention Entropy
+    eps = 1e-12
+    entropies = -np.sum(weights * np.log2(weights + eps), axis=1)
+    mean_entropy = float(np.mean(entropies))
+    max_entropy = math.log2(S) if S > 1 else 1.0
+    norm_entropy = mean_entropy / max_entropy
+
+    # 4. Hoyer Sparsity
+    l1 = np.sum(np.abs(weights), axis=1)
+    l2 = np.sqrt(np.sum(weights ** 2, axis=1))
+    sqrt_s = math.sqrt(S)
+    sparsities = (sqrt_s - (l1 / (l2 + eps))) / (sqrt_s - 1.0) if sqrt_s > 1 else np.zeros(N)
+    mean_sparsity = float(np.mean(sparsities))
+
+    summary: Dict[str, Any] = {
+        "pointing_game_hit_rate": hit_rate,
+        "cruciate_mass_fraction": mean_mass_fraction,
+        "attention_entropy_bits": mean_entropy,
+        "normalized_entropy": norm_entropy,
+        "hoyer_sparsity": mean_sparsity,
+        "total_evaluated_cases": int(N),
+        "target_slice_range": f"Slices {start_s} to {end_s} (of {S})",
+    }
+
+    if labels is not None:
+        labels = np.asarray(labels).astype(int)
+        pos_mask = (labels == 1)
+        neg_mask = (labels == 0)
+        if np.any(pos_mask):
+            summary["positive_tear_hit_rate"] = float(np.mean(hits[pos_mask]))
+            summary["positive_tear_mass_fraction"] = float(np.mean(cruciate_mass[pos_mask]))
+            summary["positive_tear_entropy"] = float(np.mean(entropies[pos_mask]))
+        if np.any(neg_mask):
+            summary["negative_knee_hit_rate"] = float(np.mean(hits[neg_mask]))
+            summary["negative_knee_mass_fraction"] = float(np.mean(cruciate_mass[neg_mask]))
+            summary["negative_knee_entropy"] = float(np.mean(entropies[neg_mask]))
+
+    return summary
+
+
+def evaluate_perturbation_faithfulness(
+    model: nn.Module,
+    val_loader,
+    device: torch.device,
+    k_slices: int = 3,
+    config=None,
+) -> Dict[str, float]:
+    """
+    Measure quantitative faithfulness of slice attention:
+    Mask out top-k slices attended by the model and record drop in predicted tear probability.
+    """
+    model.eval()
+    orig_probs = []
+    perturbed_probs = []
+    labels = []
+    use_amp = config.use_amp if config else True
+
+    with torch.no_grad():
+        for batch in val_loader:
+            sag = batch["sagittal"].to(device)
+            cor = batch["coronal"].to(device)
+            axi = batch["axial"].to(device)
+            sag_m = batch["sag_mask"].to(device)
+            cor_m = batch["cor_mask"].to(device)
+            axi_m = batch["axi_mask"].to(device)
+
+            with torch.amp.autocast("cuda", enabled=use_amp and torch.cuda.is_available()):
+                out = model(sag, cor, axi, sag_m, cor_m, axi_m)
+
+            p_orig = out["probs"].squeeze(-1).cpu().numpy()
+            orig_probs.extend(p_orig.tolist())
+            labels.extend(batch["label"].numpy().tolist())
+
+            sag_weights = out.get("sag_weights")
+            if sag_weights is not None:
+                sag_pert = sag.clone()
+                B, S = sag_weights.shape
+                for b in range(B):
+                    top_k_indices = torch.topk(sag_weights[b], k=min(k_slices, S)).indices
+                    sag_pert[b, top_k_indices] = 0.0
+
+                with torch.amp.autocast("cuda", enabled=use_amp and torch.cuda.is_available()):
+                    out_pert = model(sag_pert, cor, axi, sag_m, cor_m, axi_m)
+                p_pert = out_pert["probs"].squeeze(-1).cpu().numpy()
+                perturbed_probs.extend(p_pert.tolist())
+            else:
+                perturbed_probs.extend(p_orig.tolist())
+
+    orig = np.array(orig_probs)
+    pert = np.array(perturbed_probs)
+    y = np.array(labels)
+
+    delta_p = orig - pert
+    pos_mask = (y == 1)
+
+    return {
+        "mean_prob_drop_all": float(np.mean(delta_p)),
+        "mean_prob_drop_positive_cases": float(np.mean(delta_p[pos_mask])) if np.any(pos_mask) else 0.0,
+        "faithfulness_impact_ratio": float(np.mean(delta_p[pos_mask]) / (np.mean(orig[pos_mask]) + 1e-8)) if np.any(pos_mask) else 0.0,
+        "top_k_slices_masked": int(k_slices),
+    }
+
+
+# ── Segmentation & Localization Architecture Evaluation ─────────────
+
+def evaluate_slice_localization(
+    slice_weights: np.ndarray,
+    ground_truth_active_range: Tuple[int, int] = (10, 18),
+    threshold: Optional[float] = None,
+) -> Dict[str, float]:
+    """
+    Evaluate slice-level weak volumetric localization using Dice and IoU metrics
+    against the known central cruciate ligament anatomical zone.
+    """
+    weights = np.asarray(slice_weights).astype(float)
+    if weights.ndim == 1:
+        weights = weights.reshape(1, -1)
+    N, S = weights.shape
+
+    gt_mask = np.zeros(S, dtype=float)
+    start_s, end_s = ground_truth_active_range
+    gt_mask[start_s:end_s + 1] = 1.0
+
+    soft_dices = []
+    hard_dices = []
+    ious = []
+
+    for i in range(N):
+        w = weights[i]
+        w_norm = (w - w.min()) / (w.max() - w.min() + 1e-8)
+
+        # Soft continuous Dice
+        intersection = np.sum(w_norm * gt_mask)
+        soft_dice = (2.0 * intersection) / (np.sum(w_norm) + np.sum(gt_mask) + 1e-8)
+        soft_dices.append(soft_dice)
+
+        # Binary Dice & IoU
+        tau = threshold if threshold is not None else float(np.mean(w_norm))
+        pred_bin = (w_norm >= tau).astype(float)
+        inter_bin = np.sum(pred_bin * gt_mask)
+        union_bin = np.sum(np.maximum(pred_bin, gt_mask))
+        dice_bin = (2.0 * inter_bin) / (np.sum(pred_bin) + np.sum(gt_mask) + 1e-8)
+        iou_bin = inter_bin / (union_bin + 1e-8)
+
+        hard_dices.append(dice_bin)
+        ious.append(iou_bin)
+
+    return {
+        "slice_localization_soft_dice": float(np.mean(soft_dices)),
+        "slice_localization_hard_dice": float(np.mean(hard_dices)),
+        "slice_localization_iou": float(np.mean(ious)),
+        "anatomical_reference_range": f"Slices {start_s} to {end_s}",
+    }
+
+
+def evaluate_msm_reconstruction(
+    msm_model: nn.Module,
+    val_loader,
+    device: torch.device,
+) -> Dict[str, float]:
+    """
+    Evaluate Phase 1 Masked Slice Modeling (MSM) pretext reconstruction fidelity:
+    Computes MSE and PSNR on masked slice feature representations.
+    """
+    msm_model.eval()
+    losses = []
+
+    with torch.no_grad():
+        for batch in val_loader:
+            sag = batch["sagittal"].to(device)
+            cor = batch["coronal"].to(device)
+            axi = batch["axial"].to(device)
+            sag_m = batch["sag_mask"].to(device)
+            cor_m = batch["cor_mask"].to(device)
+            axi_m = batch["axi_mask"].to(device)
+
+            out = msm_model(sag, cor, axi, sag_m, cor_m, axi_m)
+            loss_val = out["loss"].item()
+            losses.append(loss_val)
+
+    mean_mse = float(np.mean(losses))
+    psnr = float(10.0 * np.log10(1.0 / (mean_mse + 1e-10)))
+
+    return {
+        "msm_reconstruction_mse": mean_mse,
+        "msm_reconstruction_psnr_db": psnr,
+    }
+
+
+def generate_architecture_benchmark_table(model: nn.Module, config=None) -> pd.DataFrame:
+    """Generate architectural parameter and memory complexity benchmark table."""
+    total_params = sum(p.numel() for p in model.parameters())
+    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+
+    backbone_params = sum(p.numel() for p in model.backbone.parameters()) if hasattr(model, "backbone") else 0
+    slice_attn_params = sum(p.numel() for p in model.slice_attn.parameters()) if hasattr(model, "slice_attn") and model.slice_attn else 0
+    fusion_params = sum(p.numel() for p in model.fusion.parameters()) if hasattr(model, "fusion") else 0
+    classifier_params = sum(p.numel() for p in model.classifier.parameters()) if hasattr(model, "classifier") else 0
+
+    param_df = pd.DataFrame([
+        {"Module / Component": "Shared 2D Backbone (ConvNeXt-Tiny)", "Parameters": f"{backbone_params:,}", "Param Fraction": f"{backbone_params/total_params*100:.1f}%"},
+        {"Module / Component": "Slice Attention Pooling", "Parameters": f"{slice_attn_params:,}", "Param Fraction": f"{slice_attn_params/total_params*100:.1f}%"},
+        {"Module / Component": "Cross-View Attention Fusion", "Parameters": f"{fusion_params:,}", "Param Fraction": f"{fusion_params/total_params*100:.1f}%"},
+        {"Module / Component": "Classification Head", "Parameters": f"{classifier_params:,}", "Param Fraction": f"{classifier_params/total_params*100:.1f}%"},
+        {"Module / Component": "TOTAL NETWORK", "Parameters": f"{total_params:,}", "Param Fraction": "100.0%"},
+        {"Module / Component": "Trainable Parameters", "Parameters": f"{trainable_params:,}", "Param Fraction": f"{trainable_params/total_params*100:.1f}%"},
+    ])
+
+    return param_df
+
+
+# ── Cross-Dataset Generalization & Scanner Perturbation Robustness ──
+
+def evaluate_scanner_perturbation_robustness(
+    model: nn.Module,
+    val_loader,
+    device: torch.device,
+    config=None,
+) -> Dict[str, Dict[str, float]]:
+    """
+    Stress-test cross-dataset / scanner domain shifts using realistic synthetic perturbations:
+    1. Low-Field SNR Shift: Rician noise injection (sigma=0.08) simulating 1.5T scanners.
+    2. Slice Thickness Shift: Decimating slice count by 2x and linear interpolating back.
+    3. B1 Field Bias Shift: Spatial intensity gradient simulating RF coil inhomogeneity.
+    """
+    model.eval()
+    use_amp = config.use_amp if config else True
+
+    clean_labels = []
+    clean_preds = []
+    noise_preds = []
+    thick_preds = []
+    bias_preds = []
+
+    with torch.no_grad():
+        for batch in val_loader:
+            sag = batch["sagittal"].to(device)
+            cor = batch["coronal"].to(device)
+            axi = batch["axial"].to(device)
+            sag_m = batch["sag_mask"].to(device)
+            cor_m = batch["cor_mask"].to(device)
+            axi_m = batch["axi_mask"].to(device)
+
+            clean_labels.extend(batch["label"].numpy().tolist())
+
+            # Baseline
+            with torch.amp.autocast("cuda", enabled=use_amp and torch.cuda.is_available()):
+                out_clean = model(sag, cor, axi, sag_m, cor_m, axi_m)
+            clean_preds.extend(out_clean["probs"].squeeze(-1).cpu().numpy().tolist())
+
+            # A. Rician Noise Perturbation
+            noise_sigma = 0.08
+            sag_n = torch.sqrt((sag + torch.randn_like(sag)*noise_sigma)**2 + (torch.randn_like(sag)*noise_sigma)**2)
+            cor_n = torch.sqrt((cor + torch.randn_like(cor)*noise_sigma)**2 + (torch.randn_like(cor)*noise_sigma)**2)
+            axi_n = torch.sqrt((axi + torch.randn_like(axi)*noise_sigma)**2 + (torch.randn_like(axi)*noise_sigma)**2)
+            with torch.amp.autocast("cuda", enabled=use_amp and torch.cuda.is_available()):
+                out_noise = model(sag_n, cor_n, axi_n, sag_m, cor_m, axi_m)
+            noise_preds.extend(out_noise["probs"].squeeze(-1).cpu().numpy().tolist())
+
+            # B. Slice Thickness Decimation Perturbation
+            sag_t = copy.deepcopy(sag)
+            sag_t[:, 1::2] = sag_t[:, 0::2]
+            with torch.amp.autocast("cuda", enabled=use_amp and torch.cuda.is_available()):
+                out_thick = model(sag_t, cor, axi, sag_m, cor_m, axi_m)
+            thick_preds.extend(out_thick["probs"].squeeze(-1).cpu().numpy().tolist())
+
+            # C. B1 Field Bias Field
+            H, W = sag.shape[-2], sag.shape[-1]
+            y_grad = torch.linspace(0.8, 1.2, H, device=device).unsqueeze(1).repeat(1, W)
+            sag_b = torch.clamp(sag * y_grad, 0.0, 1.0)
+            cor_b = torch.clamp(cor * y_grad, 0.0, 1.0)
+            axi_b = torch.clamp(axi * y_grad, 0.0, 1.0)
+            with torch.amp.autocast("cuda", enabled=use_amp and torch.cuda.is_available()):
+                out_bias = model(sag_b, cor_b, axi_b, sag_m, cor_m, axi_m)
+            bias_preds.extend(out_bias["probs"].squeeze(-1).cpu().numpy().tolist())
+
+    y = np.array(clean_labels)
+    base_m = compute_metrics(y, np.array(clean_preds))
+    noise_m = compute_metrics(y, np.array(noise_preds))
+    thick_m = compute_metrics(y, np.array(thick_preds))
+    bias_m = compute_metrics(y, np.array(bias_preds))
+
+    return {
+        "Baseline": {
+            "auroc": base_m["auroc"],
+            "accuracy": base_m["accuracy"],
+            "sensitivity": base_m["sensitivity"],
+            "specificity": base_m["specificity"],
+            "f1": base_m["f1"],
+        },
+        "Low_Field_SNR_Shift (Rician Noise)": {
+            "auroc": noise_m["auroc"],
+            "delta_auroc": float(noise_m["auroc"] - base_m["auroc"]),
+            "accuracy": noise_m["accuracy"],
+            "delta_accuracy": float(noise_m["accuracy"] - base_m["accuracy"]),
+        },
+        "Slice_Thickness_Decimation": {
+            "auroc": thick_m["auroc"],
+            "delta_auroc": float(thick_m["auroc"] - base_m["auroc"]),
+            "accuracy": thick_m["accuracy"],
+            "delta_accuracy": float(thick_m["accuracy"] - base_m["accuracy"]),
+        },
+        "B1_Coil_Bias_Field": {
+            "auroc": bias_m["auroc"],
+            "delta_auroc": float(bias_m["auroc"] - base_m["auroc"]),
+            "accuracy": bias_m["accuracy"],
+            "delta_accuracy": float(bias_m["accuracy"] - base_m["accuracy"]),
+        },
+    }
+
+
+def evaluate_external_dataset(
+    model: nn.Module,
+    external_dataloader,
+    device: torch.device,
+    config=None,
+    dataset_name: str = "External_Knee_Cohort",
+) -> Dict[str, Any]:
+    """Run out-of-domain evaluation on an external knee MRI dataset."""
+    model.eval()
+    preds = []
+    labels = []
+    use_amp = config.use_amp if config else True
+
+    with torch.no_grad():
+        for batch in external_dataloader:
+            sag = batch["sagittal"].to(device)
+            cor = batch["coronal"].to(device)
+            axi = batch["axial"].to(device)
+            sag_m = batch["sag_mask"].to(device)
+            cor_m = batch["cor_mask"].to(device)
+            axi_m = batch["axi_mask"].to(device)
+
+            with torch.amp.autocast("cuda", enabled=use_amp and torch.cuda.is_available()):
+                out = model(sag, cor, axi, sag_m, cor_m, axi_m)
+
+            preds.extend(out["probs"].squeeze(-1).cpu().numpy().tolist())
+            labels.extend(batch["label"].numpy().tolist())
+
+    y = np.array(labels)
+    p = np.array(preds)
+    metrics_ci = compute_metrics_with_ci(y, p)
+    metrics = compute_metrics(y, p)
+
+    return {
+        "dataset_name": dataset_name,
+        "sample_count": int(len(y)),
+        "metrics": metrics,
+        "metrics_ci": metrics_ci,
+    }
+
+
+# ── Statistical Hypothesis Tests ───────────────────────────────────
+
+def mcnemar_test(
+    labels: np.ndarray,
+    preds_a: np.ndarray,
+    preds_b: np.ndarray,
+    threshold: float = 0.5,
+) -> Tuple[float, float]:
+    """
+    McNemar's test for comparing paired classifier accuracy on the same cohort.
+    Returns: (chi2_statistic, p_value)
+    """
+    binary_a = (preds_a >= threshold).astype(int)
+    binary_b = (preds_b >= threshold).astype(int)
+
+    correct_a = (binary_a == labels)
+    correct_b = (binary_b == labels)
+
+    b = int(np.sum(correct_a & ~correct_b))
+    c = int(np.sum(~correct_a & correct_b))
+
+    if b + c == 0:
+        return 0.0, 1.0
+
+    chi2 = float((abs(b - c) - 1) ** 2 / (b + c))
+    p_value = float(1.0 - stats.chi2.cdf(chi2, df=1))
+    return chi2, p_value
+
+
+def delong_test(
+    labels: np.ndarray,
+    preds_a: np.ndarray,
+    preds_b: np.ndarray,
+) -> Tuple[float, float]:
+    """
+    DeLong's test for comparing AUROCs of two models on paired test set.
+    Returns: (z_statistic, p_value)
+    """
+    auc_a = float(roc_auc_score(labels, preds_a))
+    auc_b = float(roc_auc_score(labels, preds_b))
+
+    n1 = int(np.sum(labels == 1))
+    n0 = int(np.sum(labels == 0))
+
+    if n1 == 0 or n0 == 0:
+        return 0.0, 1.0
+
+    q1_a = auc_a / (2.0 - auc_a)
+    q2_a = 2.0 * auc_a**2 / (1.0 + auc_a)
+    se_a = np.sqrt((auc_a * (1.0 - auc_a) + (n1 - 1) * (q1_a - auc_a**2) +
+                    (n0 - 1) * (q2_a - auc_a**2)) / (n1 * n0))
+
+    q1_b = auc_b / (2.0 - auc_b)
+    q2_b = 2.0 * auc_b**2 / (1.0 + auc_b)
+    se_b = np.sqrt((auc_b * (1.0 - auc_b) + (n1 - 1) * (q1_b - auc_b**2) +
+                    (n0 - 1) * (q2_b - auc_b**2)) / (n1 * n0))
+
+    se_diff = float(np.sqrt(se_a**2 + se_b**2))
+    if se_diff == 0:
+        return 0.0, 1.0
+
+    z = float((auc_a - auc_b) / se_diff)
+    p_value = float(2.0 * (1.0 - stats.norm.cdf(abs(z))))
+    return z, p_value
+
+
+# ── Visualization Helpers ──────────────────────────────────────────
+
+def plot_roc_curve(
+    labels: np.ndarray,
+    predictions: np.ndarray,
+    save_path: Optional[str] = None,
+    title: str = "Receiver Operating Characteristic (ROC)",
+    model_name: str = "ACL-LKNet",
+) -> plt.Figure:
+    """Plot publication ROC curve."""
+    fpr, tpr, _ = roc_curve(labels, predictions)
+    auc = float(roc_auc_score(labels, predictions))
+
+    fig, ax = plt.subplots(figsize=(7, 7))
+    ax.plot(fpr, tpr, color="#0275d8", lw=2.5, label=f"{model_name} (AUROC = {auc:.3f})")
+    ax.plot([0, 1], [0, 1], color="gray", ls="--", alpha=0.6, label="Chance Reference (AUC = 0.500)")
+    ax.set_xlabel("False Positive Rate (1 - Specificity)", fontsize=12)
+    ax.set_ylabel("True Positive Rate (Sensitivity)", fontsize=12)
+    ax.set_title(title, fontsize=14, fontweight="bold")
+    ax.legend(loc="lower right", fontsize=11)
+    ax.grid(alpha=0.3)
+    plt.tight_layout()
+
+    if save_path:
+        os.makedirs(os.path.dirname(os.path.abspath(save_path)), exist_ok=True)
+        fig.savefig(save_path, dpi=200, bbox_inches="tight")
+    return fig
+
+
+def plot_precision_recall_curve(
+    labels: np.ndarray,
+    predictions: np.ndarray,
+    save_path: Optional[str] = None,
+    title: str = "Precision-Recall Curve (AUPRC)",
+    model_name: str = "ACL-LKNet",
+) -> plt.Figure:
+    """Plot publication Precision-Recall curve."""
+    precision, recall, _ = precision_recall_curve(labels, predictions)
+    auprc = float(average_precision_score(labels, predictions))
+    prevalence = float(labels.sum() / len(labels))
+
+    fig, ax = plt.subplots(figsize=(7, 7))
+    ax.plot(recall, precision, color="#5cb85c", lw=2.5, label=f"{model_name} (AUPRC = {auprc:.3f})")
+    ax.axhline(prevalence, color="gray", ls="--", alpha=0.6, label=f"Prevalence Baseline ({prevalence:.3f})")
+    ax.set_xlabel("Recall (Sensitivity)", fontsize=12)
+    ax.set_ylabel("Precision (PPV)", fontsize=12)
+    ax.set_title(title, fontsize=14, fontweight="bold")
+    ax.legend(loc="upper right", fontsize=11)
+    ax.grid(alpha=0.3)
+    plt.tight_layout()
+
+    if save_path:
+        os.makedirs(os.path.dirname(os.path.abspath(save_path)), exist_ok=True)
+        fig.savefig(save_path, dpi=200, bbox_inches="tight")
+    return fig
+
+
+def plot_training_curves(
+    train_history: List[dict],
+    val_history: List[dict],
+    save_path: Optional[str] = None,
+) -> plt.Figure:
+    """Plot multi-panel training and validation dynamics."""
+    fig, axes = plt.subplots(1, 3, figsize=(18, 5))
+    train_epochs = [h.get("epoch", i) for i, h in enumerate(train_history)]
+    val_epochs = [h.get("epoch", i) for i, h in enumerate(val_history)]
+
+    # Loss
+    if train_history and "train_loss" in train_history[0]:
+        axes[0].plot(train_epochs, [h["train_loss"] for h in train_history], label="Train Loss", color="#0275d8", lw=2)
+    if val_history and "val_loss" in val_history[0]:
+        axes[0].plot(val_epochs, [h["val_loss"] for h in val_history], label="Val Loss", color="#d9534f", lw=2)
+    axes[0].set_title("Cross-Entropy / BCE Loss", fontsize=13, fontweight="bold")
+    axes[0].set_xlabel("Epoch", fontsize=11)
+    axes[0].legend()
+    axes[0].grid(alpha=0.3)
+
+    # AUROC
+    if val_history and "val_auroc" in val_history[0]:
+        axes[1].plot(val_epochs, [h["val_auroc"] for h in val_history], label="Validation AUROC", color="#5cb85c", lw=2)
+        axes[1].axhline(0.95, color="k", ls="--", alpha=0.5, label="Target (0.95)")
+    axes[1].set_title("Validation AUROC", fontsize=13, fontweight="bold")
+    axes[1].set_xlabel("Epoch", fontsize=11)
+    axes[1].legend()
+    axes[1].grid(alpha=0.3)
+
+    # Accuracy
+    if val_history and "val_accuracy" in val_history[0]:
+        axes[2].plot(val_epochs, [h["val_accuracy"] for h in val_history], label="Validation Accuracy", color="#f0ad4e", lw=2)
+        axes[2].axhline(0.90, color="k", ls="--", alpha=0.5, label="Target (0.90)")
+    axes[2].set_title("Validation Accuracy", fontsize=13, fontweight="bold")
+    axes[2].set_xlabel("Epoch", fontsize=11)
+    axes[2].legend()
+    axes[2].grid(alpha=0.3)
+
+    plt.tight_layout()
+    if save_path:
+        os.makedirs(os.path.dirname(os.path.abspath(save_path)), exist_ok=True)
+        fig.savefig(save_path, dpi=200, bbox_inches="tight")
+    return fig
+
+
+def print_results_table(results: Dict[str, Tuple[float, float, float]]):
+    """Print clean results table with 95% Bootstrap CIs."""
+    print("\n" + "=" * 65)
+    print(f"{'Clinical Metric':<20} {'Point Estimate':>15} {'95% Bootstrap CI':>25}")
+    print("-" * 65)
+    for name, (point, lower, upper) in results.items():
+        print(f"{name:<20} {point:>15.4f} [{lower:.4f}, {upper:.4f}]")
+    print("=" * 65 + "\n")
+
+
+# ── Master Full Evaluation Orchestrator ──────────────────────────────
+
+def full_evaluation(
+    model: nn.Module,
+    val_loader,
+    config,
+    device: torch.device,
+    save_dir: Optional[str] = None,
+) -> Dict[str, Any]:
+    """
+    Execute the master scientific evaluation protocol across all 8 reviewer dimensions:
+    1. Extended diagnostic classification metrics with 95% Bootstrap CIs.
+    2. Thresholding-based evaluations (Youden's J, F1-optimal, high-sensitivity).
+    3. Dual confusion matrix generation (counts + condition-normalized).
+    4. Quantitative slice explainability (pointing game, entropy, sparsity).
+    5. Weak slice localization (Dice, IoU).
+    6. Architecture benchmark summary.
+    7. Scanner perturbation domain-shift robustness.
+    """
+    model.eval()
+    all_preds = []
+    all_labels = []
+    sag_weights_list = []
+
+    use_amp = config.use_amp if config else True
+
+    with torch.no_grad():
+        for batch in val_loader:
+            sag = batch["sagittal"].to(device)
+            cor = batch["coronal"].to(device)
+            axi = batch["axial"].to(device)
+            sag_m = batch["sag_mask"].to(device)
+            cor_m = batch["cor_mask"].to(device)
+            axi_m = batch["axi_mask"].to(device)
+
+            with torch.amp.autocast("cuda", enabled=use_amp and torch.cuda.is_available()):
+                output = model(sag, cor, axi, sag_m, cor_m, axi_m)
+
+            probs = output["probs"].squeeze(-1).cpu().numpy().tolist()
+            all_preds.extend(probs)
+            all_labels.extend(batch["label"].numpy().tolist())
+
+            if "sag_weights" in output and output["sag_weights"] is not None:
+                sag_weights_list.append(output["sag_weights"].cpu().numpy())
+
+    labels = np.array(all_labels)
+    preds = np.array(all_preds)
+
+    # 1. Classification Metrics & Bootstrap CIs
+    metrics_ci = compute_metrics_with_ci(labels, preds, n_bootstrap=config.bootstrap_n)
+    print_results_table(metrics_ci)
+
+    # 2. Thresholding-based Optimization
+    target_sens = getattr(config, "target_sensitivity", 0.95)
+    opt_thresholds = find_optimal_thresholds(labels, preds, target_sensitivity=target_sens)
+    threshold_sweep_df = generate_threshold_sweep(labels, preds)
+
+    chosen_mode = getattr(config, "eval_threshold_mode", "youden")
+    active_tau = opt_thresholds.get(chosen_mode, opt_thresholds["default"])["threshold"]
+
+    # 3. Quantitative Explainability & Localization (if slice weights exist)
+    explainability_results = {}
+    localization_results = {}
+    if len(sag_weights_list) > 0:
+        all_sag_weights = np.concatenate(sag_weights_list, axis=0)
+        explain_range = getattr(config, "explainability_target_slice_range", (10, 18))
+        explainability_results = evaluate_slice_explainability(all_sag_weights, target_slice_range=explain_range, labels=labels)
+        localization_results = evaluate_slice_localization(all_sag_weights, ground_truth_active_range=explain_range)
+
+    # 4. Scanner Perturbation Robustness
+    robustness_results = evaluate_scanner_perturbation_robustness(model, val_loader, device, config=config)
+
+    # 5. Architecture Benchmark
+    arch_df = generate_architecture_benchmark_table(model, config=config)
+
+    # 6. Save Artifacts & Visualizations
+    if save_dir:
+        os.makedirs(save_dir, exist_ok=True)
+        # Visualizations
+        plot_roc_curve(labels, preds, os.path.join(save_dir, "roc_curve.png"))
+        plot_precision_recall_curve(labels, preds, os.path.join(save_dir, "pr_curve.png"))
+        plot_confusion_matrices_dual(labels, preds, threshold=active_tau, save_path=os.path.join(save_dir, "confusion_matrix_dual.png"))
+        plot_threshold_curves(labels, preds, save_path=os.path.join(save_dir, "threshold_curves.png"), target_sensitivity=target_sens)
+
+        # Tabular data exports
+        threshold_sweep_df.to_csv(os.path.join(save_dir, "threshold_sweep.csv"), index=False)
+        arch_df.to_csv(os.path.join(save_dir, "architecture_benchmark.csv"), index=False)
+
+        # Save training configuration table if config has export methods
+        if hasattr(config, "export_config_markdown"):
+            config.export_config_markdown(os.path.join(save_dir, "training_configuration.md"))
+        if hasattr(config, "export_config_latex"):
+            config.export_config_latex(os.path.join(save_dir, "training_configuration.tex"))
+
+        # Save summary JSON
+        summary_payload = {
+            "metrics": compute_metrics(labels, preds, threshold=active_tau),
+            "metrics_ci": {k: {"point": float(v[0]), "lower_95": float(v[1]), "upper_95": float(v[2])} for k, v in metrics_ci.items()},
+            "optimal_thresholds": opt_thresholds,
+            "explainability": explainability_results,
+            "localization": localization_results,
+            "scanner_robustness": robustness_results,
+        }
+        import json
+        with open(os.path.join(save_dir, "metrics.json"), "w") as f:
+            json.dump(summary_payload, f, indent=2)
+
+    return {
+        "labels": labels,
+        "predictions": preds,
+        "metrics_ci": metrics_ci,
+        "metrics": compute_metrics(labels, preds, threshold=active_tau),
+        "optimal_thresholds": opt_thresholds,
+        "threshold_sweep": threshold_sweep_df,
+        "explainability": explainability_results,
+        "localization": localization_results,
+        "robustness": robustness_results,
+        "architecture_benchmark": arch_df,
+    }

src/models/__init__.py

@@ -0,0 +1,2 @@
+from .acl_lknet import ACLLKNet
+from .msm import MaskedSliceModeling

src/models/acl_lknet.py

@@ -0,0 +1,244 @@
+"""
+ACL-LKNet: Full model assembly.
+
+Combines all components into the complete pipeline:
+    Backbone → Slice Attention → Cross-View Fusion → Classifier
+
+Design:
+- Shared backbone processes slices from all 3 views (parameter-efficient)
+- Slices are processed in memory-efficient chunks (for T4 GPU)
+- Supports switching between attention/concat fusion and pool/attention aggregation
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.checkpoint import checkpoint as grad_checkpoint
+
+from .backbone import create_backbone
+from .slice_attention import SliceAttention
+from .cross_view_fusion import create_fusion
+
+
+class ACLLKNet(nn.Module):
+    """
+    ACL-LKNet: Hierarchical Self-Supervised Large-Kernel Network
+    for ACL Tear Detection in Knee MRI.
+    
+    Architecture:
+        1. Shared LKNet backbone extracts per-slice features
+        2. Slice attention aggregates variable-length slice sequences per view
+        3. Cross-view attention fuses the 3 plane embeddings
+        4. Classification head outputs ACL tear probability
+    """
+
+    def __init__(
+        self,
+        backbone_name: str = "convnext_tiny",
+        pretrained: bool = True,
+        feature_dim: int = 0,
+        attn_hidden_dim: int = 256,
+        fusion_type: str = "attention",
+        fusion_num_heads: int = 2,
+        fusion_dropout: float = 0.1,
+        classifier_hidden: int = 256,
+        classifier_dropout: float = 0.3,
+        aggregation: str = "attention",  # 'attention', 'max', 'mean'
+        use_grad_checkpoint: bool = True,
+        slice_chunk_size: int = 8,
+        use_large_kernels: bool = False,
+        large_kernel_sizes: list = None,
+    ):
+        super().__init__()
+        self.slice_chunk_size = slice_chunk_size
+        self.aggregation_type = aggregation
+        self.use_grad_checkpoint = use_grad_checkpoint
+
+        # ── Backbone ──
+        self.backbone = create_backbone(
+            name=backbone_name,
+            pretrained=pretrained,
+            use_grad_checkpoint=use_grad_checkpoint,
+            use_large_kernels=use_large_kernels,
+            large_kernel_sizes=large_kernel_sizes,
+        )
+        feat_dim = feature_dim if feature_dim > 0 else self.backbone.feature_dim
+
+        # ── Slice Attention (per view) ──
+        if aggregation == "attention":
+            self.slice_attn = SliceAttention(feat_dim, attn_hidden_dim)
+        else:
+            self.slice_attn = None
+
+        # ── Cross-View Fusion ──
+        self.fusion = create_fusion(
+            fusion_type=fusion_type,
+            feature_dim=feat_dim,
+            num_heads=fusion_num_heads,
+            dropout=fusion_dropout,
+        )
+
+        # ── Classification Head ──
+        self.classifier = nn.Sequential(
+            nn.LayerNorm(feat_dim),
+            nn.Linear(feat_dim, classifier_hidden),
+            nn.GELU(),
+            nn.Dropout(classifier_dropout),
+            nn.Linear(classifier_hidden, 1),
+        )
+
+        self._feat_dim = feat_dim
+
+    @property
+    def feature_dim(self):
+        return self._feat_dim
+
+    def _extract_slice_features(self, slices: torch.Tensor) -> torch.Tensor:
+        """
+        Extract features from a batch of slices, processing in memory-efficient chunks.
+        
+        Args:
+            slices: (B, S, H, W) — grayscale MRI slices for ONE view
+        
+        Returns:
+            features: (B, S, D) — per-slice feature vectors
+        """
+        B, S, H, W = slices.shape
+        all_features = []
+
+        for i in range(0, S, self.slice_chunk_size):
+            chunk = slices[:, i : i + self.slice_chunk_size]  # (B, chunk, H, W)
+            chunk_size = chunk.shape[1]
+
+            # Reshape: (B, chunk, H, W) → (B*chunk, 1, H, W)
+            chunk = chunk.reshape(B * chunk_size, 1, H, W)
+
+            # Forward through backbone
+            feat = self.backbone(chunk)  # (B*chunk, D)
+
+            # Reshape back: (B, chunk, D)
+            feat = feat.reshape(B, chunk_size, -1)
+            all_features.append(feat)
+
+        features = torch.cat(all_features, dim=1)  # (B, S, D)
+        return features
+
+    def _aggregate_slices(
+        self, features: torch.Tensor, mask: torch.Tensor = None
+    ) -> tuple:
+        """
+        Aggregate slice features into a single view embedding.
+        
+        Args:
+            features: (B, S, D)
+            mask: (B, S) — True for valid slices
+        
+        Returns:
+            embedding: (B, D)
+            attn_weights: (B, S) or None
+        """
+        if self.aggregation_type == "attention" and self.slice_attn is not None:
+            return self.slice_attn(features, mask)
+        elif self.aggregation_type == "max":
+            if mask is not None:
+                features = features.masked_fill(~mask.unsqueeze(-1), float("-inf"))
+            return features.max(dim=1)[0], None
+        elif self.aggregation_type == "mean":
+            if mask is not None:
+                features = features * mask.unsqueeze(-1).float()
+                return features.sum(dim=1) / mask.sum(dim=1, keepdim=True).float(), None
+            return features.mean(dim=1), None
+        else:
+            raise ValueError(f"Unknown aggregation: {self.aggregation_type}")
+
+    def forward(
+        self,
+        sagittal: torch.Tensor,
+        coronal: torch.Tensor,
+        axial: torch.Tensor,
+        sag_mask: torch.Tensor = None,
+        cor_mask: torch.Tensor = None,
+        axi_mask: torch.Tensor = None,
+    ) -> dict:
+        """
+        Full forward pass.
+        
+        Args:
+            sagittal: (B, S_sag, H, W) — sagittal MRI slices
+            coronal:  (B, S_cor, H, W) — coronal MRI slices
+            axial:    (B, S_axi, H, W) — axial MRI slices
+            sag_mask: (B, S_sag) — optional padding mask
+            cor_mask: (B, S_cor) — optional padding mask
+            axi_mask: (B, S_axi) — optional padding mask
+        
+        Returns:
+            dict with:
+                'logits': (B, 1) — raw logits
+                'probs': (B, 1) — sigmoid probabilities
+                'sag_weights': (B, S_sag) — slice attention weights
+                'cor_weights': (B, S_cor) — slice attention weights
+                'axi_weights': (B, S_axi) — slice attention weights
+        """
+        # Extract slice features (shared backbone)
+        sag_feats = self._extract_slice_features(sagittal)
+        cor_feats = self._extract_slice_features(coronal)
+        axi_feats = self._extract_slice_features(axial)
+
+        # Aggregate slices → view embeddings
+        sag_emb, sag_w = self._aggregate_slices(sag_feats, sag_mask)
+        cor_emb, cor_w = self._aggregate_slices(cor_feats, cor_mask)
+        axi_emb, axi_w = self._aggregate_slices(axi_feats, axi_mask)
+
+        # Cross-view fusion
+        fused = self.fusion(sag_emb, cor_emb, axi_emb)  # (B, D)
+
+        # Classification
+        logits = self.classifier(fused)  # (B, 1)
+        probs = torch.sigmoid(logits)
+
+        return {
+            "logits": logits,
+            "probs": probs,
+            "sag_weights": sag_w,
+            "cor_weights": cor_w,
+            "axi_weights": axi_w,
+        }
+
+    def get_slice_features(
+        self,
+        sagittal: torch.Tensor,
+        coronal: torch.Tensor,
+        axial: torch.Tensor,
+    ) -> dict:
+        """
+        Extract slice features only (for MSM pretraining).
+        Does not run attention/fusion/classifier.
+        
+        Returns:
+            dict with 'sagittal', 'coronal', 'axial' — each (B, S, D)
+        """
+        return {
+            "sagittal": self._extract_slice_features(sagittal),
+            "coronal": self._extract_slice_features(coronal),
+            "axial": self._extract_slice_features(axial),
+        }
+
+
+def create_model_from_config(config) -> ACLLKNet:
+    """Create an ACLLKNet model from a Config object."""
+    return ACLLKNet(
+        backbone_name=config.backbone,
+        pretrained=config.pretrained,
+        feature_dim=config.feature_dim,
+        attn_hidden_dim=config.attn_hidden_dim,
+        fusion_type=config.fusion_type,
+        fusion_num_heads=config.fusion_num_heads,
+        fusion_dropout=config.fusion_dropout,
+        classifier_hidden=config.classifier_hidden,
+        classifier_dropout=config.classifier_dropout,
+        aggregation=getattr(config, "aggregation", "attention"),
+        use_grad_checkpoint=config.grad_checkpoint,
+        slice_chunk_size=config.slice_chunk_size,
+        use_large_kernels=config.use_large_kernels,
+        large_kernel_sizes=config.large_kernel_sizes,
+    )

src/models/backbone.py

@@ -0,0 +1,277 @@
+"""
+Backbone feature extractor for ACL-LKNet.
+
+Uses timm for standard pretrained backbones (ConvNeXt, ResNet, EfficientNet).
+Includes optional large-kernel modification for kernel-size ablation.
+
+Design decisions:
+- ConvNeXt-Tiny is the default (28M params, 7×7 DW conv, T4-friendly)
+- ResNet-50 included for RadImageNet comparison (RadImageNet provides ResNet50 weights)
+- Large-kernel variant replaces ConvNeXt DW convolutions with larger kernels
+- All backbones output a single feature vector per input image via global avg pool
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import timm
+from torch.utils.checkpoint import checkpoint as grad_checkpoint
+
+
+class BackboneWrapper(nn.Module):
+    """
+    Wraps a timm backbone to:
+    1. Accept 1-channel grayscale input (converts to 3-channel)
+    2. Strip the classification head
+    3. Return a feature vector via global average pooling
+    4. Support gradient checkpointing for T4 memory
+    """
+
+    def __init__(
+        self,
+        name: str = "convnext_tiny",
+        pretrained: bool = True,
+        use_grad_checkpoint: bool = True,
+    ):
+        super().__init__()
+        self.name = name
+        self.use_grad_checkpoint = use_grad_checkpoint
+
+        # Create backbone from timm (no classification head)
+        self.backbone = timm.create_model(
+            name,
+            pretrained=pretrained,
+            num_classes=0,       # Remove classifier → feature extractor
+            global_pool="avg",   # Global average pooling
+        )
+        self.feature_dim = self.backbone.num_features
+
+        # 1-channel → 3-channel adapter
+        # We use a lightweight conv instead of simple replication so the model
+        # can learn an optimal channel mapping for grayscale MRI
+        self.channel_adapter = nn.Sequential(
+            nn.Conv2d(1, 3, kernel_size=1, bias=False),
+            nn.BatchNorm2d(3),
+        )
+        # Initialize adapter to approximate channel replication
+        nn.init.constant_(self.channel_adapter[0].weight, 1.0 / 3.0)
+
+        if use_grad_checkpoint and hasattr(self.backbone, "set_grad_checkpointing"):
+            self.backbone.set_grad_checkpointing(enable=True)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            x: (B, 1, H, W) grayscale MRI slices
+        Returns:
+            features: (B, D) feature vectors
+        """
+        # Grayscale → 3-channel
+        x = self.channel_adapter(x)
+        # Extract features
+        features = self.backbone(x)
+        return features
+
+
+class LargeKernelBlock(nn.Module):
+    """
+    Large-kernel depth-wise convolution block inspired by RepLKNet.
+    
+    Uses depth-wise separable convolution with a large kernel,
+    plus SE (Squeeze-and-Excitation) channel attention.
+    
+    For efficiency, large DW convolutions are decomposed into:
+    depth-wise conv (large kernel) + point-wise conv (1×1)
+    The large DW conv has very few parameters (kernel_size² × channels).
+    """
+
+    def __init__(self, dim: int, kernel_size: int = 31, drop_path: float = 0.0):
+        super().__init__()
+        padding = kernel_size // 2
+
+        self.norm = nn.BatchNorm2d(dim)
+        # Large-kernel depth-wise convolution
+        self.dw_conv = nn.Conv2d(
+            dim, dim, kernel_size=kernel_size, padding=padding,
+            groups=dim, bias=False
+        )
+        # SE attention
+        self.se = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Flatten(),
+            nn.Linear(dim, dim // 4),
+            nn.GELU(),
+            nn.Linear(dim // 4, dim),
+            nn.Sigmoid(),
+        )
+        # Point-wise (1×1) expansion
+        self.pw_conv1 = nn.Conv2d(dim, dim * 4, kernel_size=1)
+        self.act = nn.GELU()
+        self.pw_conv2 = nn.Conv2d(dim * 4, dim, kernel_size=1)
+
+        # Stochastic depth
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        residual = x
+        x = self.norm(x)
+        x = self.dw_conv(x)
+        # SE attention
+        se_weight = self.se(x).unsqueeze(-1).unsqueeze(-1)
+        x = x * se_weight
+        # FFN
+        x = self.pw_conv1(x)
+        x = self.act(x)
+        x = self.pw_conv2(x)
+        x = self.drop_path(x) + residual
+        return x
+
+
+class DropPath(nn.Module):
+    """Stochastic depth — drops entire residual branches during training."""
+
+    def __init__(self, drop_prob: float = 0.0):
+        super().__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if not self.training or self.drop_prob == 0.0:
+            return x
+        keep_prob = 1 - self.drop_prob
+        shape = (x.shape[0],) + (1,) * (x.ndim - 1)
+        mask = torch.empty(shape, device=x.device).bernoulli_(keep_prob)
+        return x * mask / keep_prob
+
+
+class LKNetBackbone(nn.Module):
+    """
+    Custom Large-Kernel Network backbone for kernel-size ablation.
+    
+    4-stage hierarchical design with configurable kernel sizes per stage.
+    Used when we need to isolate the effect of kernel size independently
+    of the backbone architecture (ConvNeXt vs ResNet, etc.).
+    
+    Stage dims: [64, 128, 256, 512]
+    Stage depths: [2, 2, 6, 2]
+    Default kernels: [7, 13, 21, 31]
+    """
+
+    def __init__(
+        self,
+        in_channels: int = 1,
+        dims: list = None,
+        depths: list = None,
+        kernel_sizes: list = None,
+        drop_path_rate: float = 0.1,
+    ):
+        super().__init__()
+        dims = dims or [64, 128, 256, 512]
+        depths = depths or [2, 2, 6, 2]
+        kernel_sizes = kernel_sizes or [7, 13, 21, 31]
+
+        self.feature_dim = dims[-1]
+
+        # Stem: 4× downsampling with small kernels (stable)
+        self.stem = nn.Sequential(
+            nn.Conv2d(in_channels, dims[0], kernel_size=4, stride=4),
+            nn.BatchNorm2d(dims[0]),
+        )
+
+        # Build stages
+        self.stages = nn.ModuleList()
+        dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
+        cur = 0
+
+        for i in range(4):
+            # Downsampling between stages (except first)
+            if i > 0:
+                downsample = nn.Sequential(
+                    nn.BatchNorm2d(dims[i - 1]),
+                    nn.Conv2d(dims[i - 1], dims[i], kernel_size=2, stride=2),
+                )
+            else:
+                downsample = nn.Identity()
+
+            # Stack of LK blocks
+            blocks = nn.Sequential(*[
+                LargeKernelBlock(dims[i], kernel_sizes[i], dp_rates[cur + j])
+                for j in range(depths[i])
+            ])
+            cur += depths[i]
+
+            self.stages.append(nn.Sequential(downsample, blocks))
+
+        self.norm = nn.LayerNorm(dims[-1])
+        self.pool = nn.AdaptiveAvgPool2d(1)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            x: (B, C, H, W)
+        Returns:
+            features: (B, feature_dim)
+        """
+        x = self.stem(x)
+        for stage in self.stages:
+            x = stage(x)
+        x = self.pool(x).flatten(1)
+        x = self.norm(x)
+        return x
+
+
+def create_backbone(
+    name: str = "convnext_tiny",
+    pretrained: bool = True,
+    use_grad_checkpoint: bool = True,
+    use_large_kernels: bool = False,
+    large_kernel_sizes: list = None,
+) -> nn.Module:
+    """
+    Factory function to create a backbone feature extractor.
+    
+    Args:
+        name: Backbone name ('convnext_tiny', 'resnet18', 'resnet50',
+              'efficientnet_b0', 'lknet')
+        pretrained: Load ImageNet pretrained weights (for timm models)
+        use_grad_checkpoint: Enable gradient checkpointing
+        use_large_kernels: Replace DW convolutions with larger kernels
+        large_kernel_sizes: Kernel sizes per stage [s1, s2, s3, s4]
+    
+    Returns:
+        backbone: nn.Module with .feature_dim attribute
+    """
+    if name == "lknet":
+        # Custom LK backbone (no pretrained weights — train from scratch or MSM)
+        backbone = LKNetBackbone(
+            in_channels=1,
+            kernel_sizes=large_kernel_sizes or [7, 13, 21, 31],
+        )
+        return backbone
+
+    # Standard timm backbone
+    backbone = BackboneWrapper(
+        name=name,
+        pretrained=pretrained,
+        use_grad_checkpoint=use_grad_checkpoint,
+    )
+    return backbone
+
+
+def load_radimagenet_weights(model: nn.Module, weights_path: str):
+    """
+    Load RadImageNet pretrained weights into a ResNet/DenseNet backbone.
+    
+    RadImageNet provides weights for ResNet50, DenseNet121, InceptionV3.
+    These are loaded into the backbone's internal model.
+    
+    Args:
+        model: BackboneWrapper with a timm ResNet50 backbone
+        weights_path: Path to RadImageNet .pt/.h5 weights file
+    """
+    state_dict = torch.load(weights_path, map_location="cpu", weights_only=True)
+    # RadImageNet weights may have different key names — attempt flexible loading
+    model_dict = model.backbone.state_dict()
+    filtered = {k: v for k, v in state_dict.items() if k in model_dict and v.shape == model_dict[k].shape}
+    model_dict.update(filtered)
+    model.backbone.load_state_dict(model_dict, strict=False)
+    print(f"Loaded {len(filtered)}/{len(model_dict)} layers from RadImageNet weights")

src/models/cross_view_fusion.py

@@ -0,0 +1,135 @@
+"""
+Cross-View Attention Fusion for ACL-LKNet.
+
+Fuses plane-level representations from sagittal, coronal, and axial views
+using multi-head attention. This allows the model to learn cross-view
+dependencies rather than merely concatenating independent features.
+
+Supports two modes:
+- 'attention': Multi-head self-attention over 3 view embeddings
+- 'concat': Simple concatenation + FC (ablation baseline)
+"""
+
+import torch
+import torch.nn as nn
+
+
+class CrossViewAttentionFusion(nn.Module):
+    """
+    Fuses 3 plane embeddings using multi-head self-attention.
+    
+    Input: 3 view embeddings, each of shape (B, D)
+    Process:
+        1. Stack into sequence of length 3: (B, 3, D)
+        2. Add learnable view position embeddings
+        3. Multi-head self-attention
+        4. Mean-pool the 3 output embeddings → (B, D)
+    """
+
+    def __init__(
+        self,
+        feature_dim: int,
+        num_heads: int = 2,
+        dropout: float = 0.1,
+    ):
+        super().__init__()
+        self.feature_dim = feature_dim
+
+        # Learnable position embeddings for each view
+        # (sagittal=0, coronal=1, axial=2)
+        self.view_pos_embed = nn.Parameter(torch.zeros(1, 3, feature_dim))
+        nn.init.trunc_normal_(self.view_pos_embed, std=0.02)
+
+        # Pre-norm
+        self.norm = nn.LayerNorm(feature_dim)
+
+        # Multi-head self-attention
+        self.mha = nn.MultiheadAttention(
+            embed_dim=feature_dim,
+            num_heads=num_heads,
+            dropout=dropout,
+            batch_first=True,
+        )
+
+        # Post-attention FFN
+        self.ffn = nn.Sequential(
+            nn.LayerNorm(feature_dim),
+            nn.Linear(feature_dim, feature_dim * 2),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(feature_dim * 2, feature_dim),
+            nn.Dropout(dropout),
+        )
+
+    def forward(
+        self,
+        sagittal: torch.Tensor,
+        coronal: torch.Tensor,
+        axial: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Args:
+            sagittal: (B, D) sagittal plane embedding
+            coronal:  (B, D) coronal plane embedding
+            axial:    (B, D) axial plane embedding
+        
+        Returns:
+            fused: (B, D) fused representation
+        """
+        # Stack into sequence: (B, 3, D)
+        x = torch.stack([sagittal, coronal, axial], dim=1)
+
+        # Add view position embeddings
+        x = x + self.view_pos_embed
+
+        # Self-attention with residual
+        x_norm = self.norm(x)
+        attn_out, _ = self.mha(x_norm, x_norm, x_norm)
+        x = x + attn_out
+
+        # FFN with residual
+        x = x + self.ffn(x)
+
+        # Mean-pool over the 3 views
+        fused = x.mean(dim=1)  # (B, D)
+        return fused
+
+
+class ConcatFusion(nn.Module):
+    """
+    Simple concatenation + FC fusion (ablation baseline).
+    
+    Concatenates 3 view embeddings → FC → output embedding.
+    """
+
+    def __init__(self, feature_dim: int, dropout: float = 0.1):
+        super().__init__()
+        self.fusion = nn.Sequential(
+            nn.Linear(feature_dim * 3, feature_dim),
+            nn.GELU(),
+            nn.Dropout(dropout),
+        )
+
+    def forward(
+        self,
+        sagittal: torch.Tensor,
+        coronal: torch.Tensor,
+        axial: torch.Tensor,
+    ) -> torch.Tensor:
+        x = torch.cat([sagittal, coronal, axial], dim=-1)  # (B, 3D)
+        return self.fusion(x)  # (B, D)
+
+
+def create_fusion(
+    fusion_type: str,
+    feature_dim: int,
+    num_heads: int = 2,
+    dropout: float = 0.1,
+) -> nn.Module:
+    """Factory for fusion modules."""
+    if fusion_type == "attention":
+        return CrossViewAttentionFusion(feature_dim, num_heads, dropout)
+    elif fusion_type == "concat":
+        return ConcatFusion(feature_dim, dropout)
+    else:
+        raise ValueError(f"Unknown fusion type: {fusion_type}")

src/models/msm.py

@@ -0,0 +1,212 @@
+"""
+Masked Slice Modeling (MSM) — Self-Supervised Pretraining for ACL-LKNet.
+
+This is the PRIMARY RESEARCH CONTRIBUTION.
+
+Core idea: MRI volumes have ordered slices with anatomical continuity.
+We exploit this structure by masking some slices and training the model
+to reconstruct their features from the remaining (unmasked) context.
+
+Research question:
+    Can a self-supervised objective that explicitly models inter-slice
+    anatomical context produce better transferable representations for
+    ACL injury detection than established pretraining?
+
+Masking strategies (research axis):
+    - random:      Mask 50% of slices uniformly at random
+    - contiguous:  Mask contiguous blocks of 3-5 adjacent slices
+    - structured:  Preferentially mask central slices (clinically relevant)
+    - mixed:       Alternate random and contiguous per batch
+"""
+
+import math
+import random as py_random
+from typing import Tuple, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class MaskedSliceModeling(nn.Module):
+    """
+    Self-supervised pretraining via Masked Slice Modeling.
+    
+    Architecture:
+        1. Encode all slices through the shared backbone → slice features
+        2. Replace masked slice features with learnable [MASK] tokens
+        3. Add positional encoding (sinusoidal — respects slice ordering)
+        4. Pass through lightweight Transformer decoder
+        5. Predict the original features of masked slices
+    
+    Loss: MSE between predicted and actual features of masked slices
+    """
+
+    def __init__(
+        self,
+        feature_dim: int,
+        decoder_dim: int = 256,
+        decoder_layers: int = 2,
+        decoder_heads: int = 4,
+        max_slices: int = 48,
+        mask_ratio: float = 0.5,
+        mask_strategy: str = "random",
+    ):
+        super().__init__()
+        self.feature_dim = feature_dim
+        self.decoder_dim = decoder_dim
+        self.mask_ratio = mask_ratio
+        self.mask_strategy = mask_strategy
+        self.max_slices = max_slices
+
+        # Learnable [MASK] token
+        self.mask_token = nn.Parameter(torch.zeros(1, 1, feature_dim))
+        nn.init.trunc_normal_(self.mask_token, std=0.02)
+
+        # Project encoder features → decoder dimension
+        self.encoder_to_decoder = nn.Linear(feature_dim, decoder_dim)
+
+        # Sinusoidal positional encoding (respects spatial ordering of slices)
+        self.register_buffer(
+            "pos_encoding", self._sinusoidal_encoding(max_slices, decoder_dim)
+        )
+
+        # Lightweight Transformer decoder
+        decoder_layer = nn.TransformerEncoderLayer(
+            d_model=decoder_dim,
+            nhead=decoder_heads,
+            dim_feedforward=decoder_dim * 4,
+            dropout=0.1,
+            activation="gelu",
+            batch_first=True,
+            norm_first=True,
+        )
+        self.decoder = nn.TransformerEncoder(
+            decoder_layer,
+            num_layers=decoder_layers,
+        )
+
+        # Predict original features from decoded representations
+        self.predictor = nn.Sequential(
+            nn.LayerNorm(decoder_dim),
+            nn.Linear(decoder_dim, feature_dim),
+        )
+
+    @staticmethod
+    def _sinusoidal_encoding(max_len: int, dim: int) -> torch.Tensor:
+        """Generate sinusoidal positional encoding."""
+        pe = torch.zeros(max_len, dim)
+        position = torch.arange(0, max_len).unsqueeze(1).float()
+        div_term = torch.exp(
+            torch.arange(0, dim, 2).float() * (-math.log(10000.0) / dim)
+        )
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        return pe.unsqueeze(0)  # (1, max_len, dim)
+
+    def generate_mask(
+        self, num_slices: int, strategy: Optional[str] = None
+    ) -> torch.Tensor:
+        """
+        Generate a binary mask indicating which slices to mask.
+        
+        Args:
+            num_slices: Number of slices in the volume
+            strategy: Override the default masking strategy
+            
+        Returns:
+            mask: (num_slices,) boolean tensor. True = masked (to predict)
+        """
+        strategy = strategy or self.mask_strategy
+        num_mask = max(1, int(num_slices * self.mask_ratio))
+        # Always keep at least 2 slices unmasked for context
+        num_mask = min(num_mask, num_slices - 2)
+
+        mask = torch.zeros(num_slices, dtype=torch.bool)
+
+        if strategy == "random":
+            indices = torch.randperm(num_slices)[:num_mask]
+            mask[indices] = True
+
+        elif strategy == "contiguous":
+            # Mask contiguous blocks of 3-5 slices
+            remaining = num_mask
+            while remaining > 0:
+                block_size = min(py_random.randint(3, 5), remaining)
+                max_start = num_slices - block_size
+                if max_start <= 0:
+                    start = 0
+                else:
+                    start = py_random.randint(0, max_start)
+                mask[start : start + block_size] = True
+                remaining = num_mask - mask.sum().item()
+
+        elif strategy == "structured":
+            # Preferentially mask central slices (where ACL is typically visible)
+            center = num_slices // 2
+            # Create probability distribution peaked at center
+            positions = torch.arange(num_slices).float()
+            probs = torch.exp(-0.5 * ((positions - center) / (num_slices / 4)) ** 2)
+            probs = probs / probs.sum()
+            indices = torch.multinomial(probs, num_mask, replacement=False)
+            mask[indices] = True
+
+        elif strategy == "mixed":
+            # Randomly choose between random and contiguous per call
+            sub_strategy = py_random.choice(["random", "contiguous"])
+            mask = self.generate_mask(num_slices, strategy=sub_strategy)
+
+        else:
+            raise ValueError(f"Unknown mask strategy: {strategy}")
+
+        return mask
+
+    def forward(
+        self,
+        slice_features: torch.Tensor,
+        slice_mask: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Forward pass for MSM pretraining.
+        
+        Args:
+            slice_features: (B, S, D) — encoded slice features from backbone
+            slice_mask: (B, S) — optional padding mask (True = valid)
+            
+        Returns:
+            loss: scalar MSE loss on masked slices
+            predictions: (B, S, D) — predicted features for all slices
+            mask: (B, S) — boolean mask of which slices were masked
+        """
+        B, S, D = slice_features.shape
+
+        # Generate masks for each sample in the batch
+        masks = torch.stack([self.generate_mask(S) for _ in range(B)])  # (B, S)
+        masks = masks.to(slice_features.device)
+
+        # Replace masked positions with [MASK] token
+        mask_tokens = self.mask_token.expand(B, S, -1)  # (B, S, D)
+        masked_features = slice_features.clone()
+        masked_features[masks] = mask_tokens[masks]
+
+        # Project to decoder dimension
+        x = self.encoder_to_decoder(masked_features)  # (B, S, decoder_dim)
+
+        # Add positional encoding
+        x = x + self.pos_encoding[:, :S, :]
+
+        # Transformer decoder
+        x = self.decoder(x)  # (B, S, decoder_dim)
+
+        # Predict original features
+        predictions = self.predictor(x)  # (B, S, D)
+
+        # Compute loss only on masked positions
+        if masks.any():
+            pred_masked = predictions[masks]     # (num_masked, D)
+            target_masked = slice_features[masks]  # (num_masked, D)
+            loss = F.mse_loss(pred_masked, target_masked)
+        else:
+            loss = torch.tensor(0.0, device=slice_features.device)
+
+        return loss, predictions, masks

src/models/slice_attention.py

@@ -0,0 +1,117 @@
+"""
+Slice Attention module for ACL-LKNet.
+
+Aggregates a variable-length sequence of slice features into a single
+exam-level representation using learned attention weights.
+
+Why attention over max-pool:
+- Max-pool discards information about WHICH slices are informative
+- Attention learns to weight slices showing pathology more heavily
+- Attention weights are interpretable (can visualize which slices matter)
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class SliceAttention(nn.Module):
+    """
+    Attention-based aggregation over a sequence of slice features.
+    
+    Given slice features [f_1, f_2, ..., f_S], computes:
+        α_i = softmax(w^T · tanh(W_1 · f_i + b_1))
+        v = Σ α_i · f_i
+    
+    This is Bahdanau-style (additive) attention with a single attention head.
+    """
+
+    def __init__(self, feature_dim: int, hidden_dim: int = 256):
+        """
+        Args:
+            feature_dim: Dimension of input slice features
+            hidden_dim: Hidden dimension in attention computation
+        """
+        super().__init__()
+        self.attention = nn.Sequential(
+            nn.Linear(feature_dim, hidden_dim),
+            nn.Tanh(),
+            nn.Linear(hidden_dim, 1),
+        )
+
+    def forward(
+        self, features: torch.Tensor, mask: torch.Tensor = None
+    ) -> tuple:
+        """
+        Args:
+            features: (B, S, D) — batch of slice feature sequences
+            mask: (B, S) — optional boolean mask (True = valid slice)
+        
+        Returns:
+            aggregated: (B, D) — attention-weighted feature vector
+            weights: (B, S) — attention weights (for visualization)
+        """
+        # Compute attention scores
+        scores = self.attention(features).squeeze(-1)  # (B, S)
+
+        # Mask invalid slices (from padding)
+        if mask is not None:
+            scores = scores.masked_fill(~mask, float("-inf"))
+
+        # Softmax → attention weights
+        weights = F.softmax(scores, dim=1)  # (B, S)
+
+        # Weighted sum
+        aggregated = torch.bmm(
+            weights.unsqueeze(1), features
+        ).squeeze(1)  # (B, D)
+
+        return aggregated, weights
+
+
+class MultiHeadSliceAttention(nn.Module):
+    """
+    Multi-head variant of slice attention for richer aggregation.
+    
+    Each head learns to attend to different aspects of the slices
+    (e.g., one head for anatomy, another for pathology signal).
+    Outputs are concatenated and projected.
+    """
+
+    def __init__(self, feature_dim: int, num_heads: int = 4, hidden_dim: int = 256):
+        super().__init__()
+        assert feature_dim % num_heads == 0, "feature_dim must be divisible by num_heads"
+        self.num_heads = num_heads
+        self.head_dim = feature_dim // num_heads
+
+        self.heads = nn.ModuleList([
+            SliceAttention(feature_dim, hidden_dim)
+            for _ in range(num_heads)
+        ])
+        self.projection = nn.Linear(feature_dim * num_heads, feature_dim)
+
+    def forward(
+        self, features: torch.Tensor, mask: torch.Tensor = None
+    ) -> tuple:
+        """
+        Args:
+            features: (B, S, D)
+            mask: (B, S)
+        Returns:
+            aggregated: (B, D)
+            weights: (B, num_heads, S) — per-head attention weights
+        """
+        head_outputs = []
+        all_weights = []
+
+        for head in self.heads:
+            out, w = head(features, mask)
+            head_outputs.append(out)
+            all_weights.append(w)
+
+        # Concat heads and project
+        concatenated = torch.cat(head_outputs, dim=-1)  # (B, D * num_heads)
+        aggregated = self.projection(concatenated)       # (B, D)
+        weights = torch.stack(all_weights, dim=1)        # (B, num_heads, S)
+
+        return aggregated, weights

src/train.py

@@ -0,0 +1,723 @@
+"""
+Training loops for ACL-LKNet.
+
+Two phases:
+    Phase 1: SSL Pretraining (Masked Slice Modeling)
+        - Train backbone to reconstruct masked slice features
+        - Monitor pretext loss, stop when plateaus
+        
+    Phase 2: Supervised Fine-tuning
+        - Load SSL-pretrained backbone
+        - Train full model with differential LR
+        - Weighted BCE loss for class imbalance
+        - Early stopping on validation AUROC
+        
+Both phases support:
+    - Mixed precision (FP16) for T4 memory
+    - Gradient accumulation for effective batch size
+    - Gradient clipping for stability
+    - EMA model for better generalization
+    - Full checkpoint save/load for Colab session recovery
+"""
+
+import os
+import copy
+import time
+import math
+import logging
+from typing import Optional, Dict, Tuple, Any, List
+
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from .config import Config
+from .models.acl_lknet import ACLLKNet, create_model_from_config
+from .models.msm import MaskedSliceModeling
+from .dataset import create_dataloaders
+from .utils import (
+    set_seed, EMAModel, save_checkpoint, load_checkpoint,
+    find_latest_checkpoint, setup_logging, format_metrics,
+    get_gpu_memory_info, clear_gpu_memory,
+)
+from .evaluate import compute_metrics
+
+
+# ── Loss Functions ──────────────────────────────────────────────────
+
+def create_loss_fn(config: Config, device: torch.device = None) -> nn.Module:
+    """Create weighted BCE loss with label smoothing.
+    
+    Args:
+        config: Config with pos_weight setting
+        device: Target device for pos_weight tensor (avoids CPU/GPU mismatch)
+    """
+    pos_weight = torch.tensor([config.pos_weight], device=device)
+    return nn.BCEWithLogitsLoss(pos_weight=pos_weight)
+
+
+def apply_label_smoothing(labels: torch.Tensor, smoothing: float = 0.05) -> torch.Tensor:
+    """Apply label smoothing: 0 → smoothing, 1 → 1-smoothing."""
+    return labels * (1 - smoothing) + 0.5 * smoothing
+
+
+def apply_mixup(
+    batch: dict, alpha: float = 0.2
+) -> Tuple[dict, torch.Tensor, torch.Tensor, float]:
+    """
+    Apply Mixup augmentation to a batch.
+    
+    Returns modified batch, original labels, shuffled labels, and lambda.
+    """
+    if alpha <= 0:
+        return batch, batch["label"], batch["label"], 1.0
+
+    lam = np.random.beta(alpha, alpha)
+    lam = max(lam, 1 - lam)  # Ensure lam >= 0.5
+
+    B = batch["sagittal"].shape[0]
+    if B < 2:
+        return batch, batch["label"], batch["label"], 1.0
+
+    indices = torch.randperm(B)
+
+    mixed_batch = {}
+    for key in ["sagittal", "coronal", "axial"]:
+        mixed_batch[key] = lam * batch[key] + (1 - lam) * batch[key][indices]
+    for key in ["sag_mask", "cor_mask", "axi_mask"]:
+        mixed_batch[key] = batch[key]
+    mixed_batch["label"] = batch["label"]
+    mixed_batch["case_id"] = batch["case_id"]
+
+    return mixed_batch, batch["label"], batch["label"][indices], lam
+
+
+# ── Phase 1: SSL Pretraining ──────────────────────────────────────
+
+def pretrain_msm(config: Config) -> str:
+    """
+    Self-supervised pretraining via Masked Slice Modeling.
+    
+    Returns path to the best checkpoint.
+    """
+    setup_logging(config.log_dir)
+    set_seed(config.seed)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    logging.info(f"MSM Pretraining | Device: {device} | Strategy: {config.mask_strategy}")
+    logging.info(f"GPU: {get_gpu_memory_info()}")
+
+    # Data
+    train_loader, val_loader = create_dataloaders(config, ssl=True)
+    logging.info(f"Train: {len(train_loader.dataset)} exams | Val: {len(val_loader.dataset)} exams")
+
+    # Model
+    model = create_model_from_config(config).to(device)
+    msm = MaskedSliceModeling(
+        feature_dim=model.feature_dim,
+        decoder_dim=config.msm_decoder_dim,
+        decoder_layers=config.msm_decoder_layers,
+        decoder_heads=config.msm_decoder_heads,
+        max_slices=config.max_slices,
+        mask_ratio=config.mask_ratio,
+        mask_strategy=config.mask_strategy,
+    ).to(device)
+
+    # Optimizer
+    params = list(model.parameters()) + list(msm.parameters())
+    optimizer = torch.optim.AdamW(params, lr=config.ssl_lr, weight_decay=config.ssl_weight_decay)
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10, T_mult=2)
+    scaler = torch.amp.GradScaler("cuda", enabled=config.use_amp)
+
+    # Checkpoint recovery
+    start_epoch = 0
+    best_loss = float("inf")
+    train_history = []
+    val_history = []
+    patience_counter = 0
+
+    ckpt_path = find_latest_checkpoint(config.checkpoint_dir, phase="ssl")
+    if ckpt_path:
+        logging.info(f"Resuming SSL from checkpoint: {ckpt_path}")
+        ckpt = load_checkpoint(ckpt_path, model, optimizer, scheduler, scaler)
+        start_epoch = ckpt["epoch"] + 1
+        best_loss = ckpt["best_metric"]
+        train_history = ckpt.get("train_history", [])
+        val_history = ckpt.get("val_history", [])
+        patience_counter = ckpt.get("patience_counter", 0)
+        # Load MSM state if saved
+        if "msm_state_dict" in ckpt:
+            msm.load_state_dict(ckpt["msm_state_dict"])
+
+    best_ckpt_path = os.path.join(config.checkpoint_dir, "ssl_best.pt")
+
+    # Training loop
+    epoch = start_epoch
+    while True:  # No fixed epoch count — stop when loss plateaus
+        model.train()
+        msm.train()
+        epoch_loss = 0.0
+        num_batches = 0
+
+        pbar = tqdm(train_loader, desc=f"SSL Epoch {epoch}", leave=False)
+        optimizer.zero_grad()
+
+        for step, batch in enumerate(pbar):
+            sag = batch["sagittal"].to(device)
+            cor = batch["coronal"].to(device)
+            axi = batch["axial"].to(device)
+
+            with torch.amp.autocast("cuda", enabled=config.use_amp):
+                # Extract features
+                feats = model.get_slice_features(sag, cor, axi)
+
+                # MSM on each view independently
+                total_loss = 0.0
+                for view_name in ["sagittal", "coronal", "axial"]:
+                    loss, _, _ = msm(feats[view_name])
+                    total_loss = total_loss + loss
+                total_loss = total_loss / 3.0  # Average over views
+
+                # Gradient accumulation
+                total_loss = total_loss / config.accumulation_steps
+
+            scaler.scale(total_loss).backward()
+
+            if (step + 1) % config.accumulation_steps == 0:
+                scaler.unscale_(optimizer)
+                torch.nn.utils.clip_grad_norm_(params, config.gradient_clip)
+                scaler.step(optimizer)
+                scaler.update()
+                optimizer.zero_grad()
+
+            epoch_loss += total_loss.item() * config.accumulation_steps
+            num_batches += 1
+            pbar.set_postfix(loss=f"{total_loss.item() * config.accumulation_steps:.4f}")
+
+        avg_loss = epoch_loss / max(num_batches, 1)
+        scheduler.step()
+        train_history.append({"epoch": epoch, "loss": avg_loss})
+
+        # Validation
+        val_loss = _validate_msm(model, msm, val_loader, config, device)
+        val_history.append({"epoch": epoch, "loss": val_loss})
+
+        logging.info(
+            f"SSL Epoch {epoch} | Train Loss: {avg_loss:.4f} | Val Loss: {val_loss:.4f} | "
+            f"LR: {optimizer.param_groups[0]['lr']:.2e}"
+        )
+
+        # Check improvement
+        if val_loss < best_loss:
+            best_loss = val_loss
+            patience_counter = 0
+            # Save best
+            _save_ssl_checkpoint(
+                best_ckpt_path, epoch, model, msm, optimizer, scheduler,
+                scaler, best_loss, train_history, val_history, patience_counter, config,
+            )
+            logging.info(f"  ✓ New best SSL loss: {best_loss:.4f}")
+        else:
+            patience_counter += 1
+            logging.info(f"  ✗ No improvement ({patience_counter}/{config.ssl_patience})")
+
+        # Periodic save
+        if (epoch + 1) % config.ssl_save_every == 0:
+            periodic_path = os.path.join(config.checkpoint_dir, f"ssl_epoch{epoch}.pt")
+            _save_ssl_checkpoint(
+                periodic_path, epoch, model, msm, optimizer, scheduler,
+                scaler, best_loss, train_history, val_history, patience_counter, config,
+            )
+
+        # Early stopping
+        if patience_counter >= config.ssl_patience:
+            logging.info(f"SSL early stopping at epoch {epoch}")
+            break
+
+        epoch += 1
+        clear_gpu_memory()
+
+    logging.info(f"SSL pretraining complete. Best loss: {best_loss:.4f}")
+    return best_ckpt_path
+
+
+def _validate_msm(model, msm, val_loader, config, device) -> float:
+    """Run MSM validation pass."""
+    model.eval()
+    msm.eval()
+    total_loss = 0.0
+    count = 0
+
+    with torch.no_grad():
+        for batch in val_loader:
+            sag = batch["sagittal"].to(device)
+            cor = batch["coronal"].to(device)
+            axi = batch["axial"].to(device)
+
+            with torch.amp.autocast("cuda", enabled=config.use_amp):
+                feats = model.get_slice_features(sag, cor, axi)
+                loss = 0.0
+                for view_name in ["sagittal", "coronal", "axial"]:
+                    l, _, _ = msm(feats[view_name])
+                    loss = loss + l.item()
+                loss /= 3.0
+
+            total_loss += loss
+            count += 1
+
+    return total_loss / max(count, 1)
+
+
+def _save_ssl_checkpoint(path, epoch, model, msm, optimizer, scheduler, scaler,
+                          best_loss, train_history, val_history, patience_counter, config):
+    """Save SSL checkpoint including MSM state."""
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    from .utils import get_rng_states
+    checkpoint = {
+        "epoch": epoch,
+        "phase": "ssl",
+        "model_state_dict": model.state_dict(),
+        "msm_state_dict": msm.state_dict(),
+        "optimizer_state_dict": optimizer.state_dict(),
+        "scheduler_state_dict": scheduler.state_dict(),
+        "scaler_state_dict": scaler.state_dict() if scaler else None,
+        "best_metric": best_loss,
+        "best_epoch": epoch,
+        "train_history": train_history,
+        "val_history": val_history,
+        "patience_counter": patience_counter,
+        "rng_states": get_rng_states(),
+        "config": config.to_dict(),
+    }
+    torch.save(checkpoint, path)
+
+
+def train_supervised(
+    config: Config,
+    ssl_checkpoint: Optional[str] = None,
+    train_cases: Optional[list] = None,
+    val_cases: Optional[list] = None,
+    train_split: str = "train",
+    val_split: str = "valid",
+) -> str:
+    """
+    Supervised fine-tuning for ACL tear detection.
+    
+    Args:
+        config: Training config
+        ssl_checkpoint: Path to SSL pretrained checkpoint (optional)
+        train_cases: Optional explicit list of training case IDs (for CV)
+        val_cases: Optional explicit list of validation case IDs (for CV)
+        train_split: Dataset split directory name for training
+        val_split: Dataset split directory name for validation
+    
+    Returns:
+        Path to the best checkpoint (by val AUROC)
+    """
+    setup_logging(config.log_dir)
+    set_seed(config.seed)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    logging.info(f"Supervised Training | Device: {device}")
+    logging.info(f"GPU: {get_gpu_memory_info()}")
+
+    # Data
+    train_loader, val_loader = create_dataloaders(
+        config, ssl=False, train_cases=train_cases, val_cases=val_cases,
+        train_split=train_split, val_split=val_split,
+    )
+    logging.info(f"Train: {len(train_loader.dataset)} exams | Val: {len(val_loader.dataset)} exams")
+
+    # Model
+    model = create_model_from_config(config).to(device)
+
+    # Load SSL pretrained weights
+    if ssl_checkpoint and os.path.exists(ssl_checkpoint):
+        ssl_ckpt = torch.load(ssl_checkpoint, map_location=device, weights_only=False)
+        model.load_state_dict(ssl_ckpt["model_state_dict"], strict=False)
+        logging.info(f"Loaded SSL checkpoint: {ssl_checkpoint}")
+
+
+    # Differential LR: lower for backbone, higher for new layers
+    backbone_params = list(model.backbone.parameters())
+    new_params = [p for n, p in model.named_parameters()
+                  if not n.startswith("backbone")]
+    
+    optimizer = torch.optim.AdamW([
+        {"params": backbone_params, "lr": config.backbone_lr},
+        {"params": new_params, "lr": config.lr},
+    ], weight_decay=config.weight_decay)
+
+    # Cosine Annealing scheduler (resume-safe — unlike OneCycleLR, does not
+    # crash when total_steps is exceeded after checkpoint restoration)
+    max_epochs = getattr(config, "num_epochs", 40)
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
+        optimizer,
+        T_max=max(max_epochs - config.warmup_epochs, 1),
+        eta_min=1e-7,
+    )
+
+    scaler = torch.amp.GradScaler("cuda", enabled=config.use_amp)
+    criterion = create_loss_fn(config, device=device)
+    ema = EMAModel(model, decay=config.ema_decay)
+
+    # Checkpoint recovery
+    start_epoch = 0
+    best_auc = 0.0
+    best_epoch = 0
+    train_history = []
+    val_history = []
+    patience_counter = 0
+
+    ckpt_path = find_latest_checkpoint(config.checkpoint_dir, phase="finetune")
+    if ckpt_path:
+        logging.info(f"Resuming supervised training from: {ckpt_path}")
+        ckpt = load_checkpoint(ckpt_path, model, optimizer, scheduler, scaler, ema)
+        start_epoch = ckpt["epoch"] + 1
+        best_auc = ckpt["best_metric"]
+        best_epoch = ckpt.get("best_epoch", 0)
+        train_history = ckpt.get("train_history", [])
+        val_history = ckpt.get("val_history", [])
+        patience_counter = ckpt.get("patience_counter", 0)
+
+    best_ckpt_path = os.path.join(config.checkpoint_dir, "finetune_best.pt")
+
+    # Training loop
+    epoch = start_epoch
+    while True:  # Monitor-based stopping
+        model.train()
+        epoch_loss = 0.0
+        all_preds = []
+        all_labels = []
+        num_batches = 0
+
+        pbar = tqdm(train_loader, desc=f"Epoch {epoch}", leave=False)
+        optimizer.zero_grad()
+
+        for step, batch in enumerate(pbar):
+            sag = batch["sagittal"].to(device)
+            cor = batch["coronal"].to(device)
+            axi = batch["axial"].to(device)
+            sag_m = batch["sag_mask"].to(device)
+            cor_m = batch["cor_mask"].to(device)
+            axi_m = batch["axi_mask"].to(device)
+            labels = batch["label"].to(device)
+
+            # Mixup
+            if config.mixup_alpha > 0 and epoch >= config.warmup_epochs:
+                mixed, labels_a, labels_b, lam = apply_mixup(batch, config.mixup_alpha)
+                sag = mixed["sagittal"].to(device)
+                cor = mixed["coronal"].to(device)
+                axi = mixed["axial"].to(device)
+                labels_a = labels_a.to(device)
+                labels_b = labels_b.to(device)
+            else:
+                labels_a = labels_b = labels
+                lam = 1.0
+
+            with torch.amp.autocast("cuda", enabled=config.use_amp):
+                output = model(sag, cor, axi, sag_m, cor_m, axi_m)
+                logits = output["logits"].squeeze(-1)
+
+                # Label smoothing
+                smooth_a = apply_label_smoothing(labels_a, config.label_smoothing)
+                smooth_b = apply_label_smoothing(labels_b, config.label_smoothing)
+
+                # Mixup loss
+                loss = lam * criterion(logits, smooth_a) + (1 - lam) * criterion(logits, smooth_b)
+                loss = loss / config.accumulation_steps
+
+            scaler.scale(loss).backward()
+
+            if (step + 1) % config.accumulation_steps == 0:
+                scaler.unscale_(optimizer)
+                torch.nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)
+                scaler.step(optimizer)
+                scaler.update()
+                optimizer.zero_grad()
+
+                # Update EMA
+                ema.update(model)
+
+            epoch_loss += loss.item() * config.accumulation_steps
+            all_preds.extend(output["probs"].squeeze(-1).detach().cpu().numpy().tolist())
+            all_labels.extend(labels.cpu().numpy().tolist())
+            num_batches += 1
+
+            pbar.set_postfix(loss=f"{loss.item() * config.accumulation_steps:.4f}")
+
+        # ENH-2: Flush any remaining accumulated gradients from the tail batch
+        # (when dataset size is not divisible by accumulation_steps)
+        if (step + 1) % config.accumulation_steps != 0:
+            scaler.unscale_(optimizer)
+            torch.nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)
+            scaler.step(optimizer)
+            scaler.update()
+            optimizer.zero_grad()
+            ema.update(model)
+
+        # Step the cosine annealing scheduler once per epoch (after warmup)
+        if epoch >= config.warmup_epochs:
+            scheduler.step()
+        else:
+            # Linear warmup: scale LR from 0 to target over warmup_epochs
+            warmup_factor = (epoch + 1) / config.warmup_epochs
+            for pg_idx, pg in enumerate(optimizer.param_groups):
+                base_lr = config.backbone_lr if pg_idx == 0 else config.lr
+                pg['lr'] = base_lr * warmup_factor
+
+        avg_loss = epoch_loss / max(num_batches, 1)
+
+        # Train metrics
+        train_metrics = compute_metrics(
+            np.array(all_labels), np.array(all_preds), prefix="train"
+        )
+        train_metrics["train_loss"] = avg_loss
+        train_history.append({"epoch": epoch, **train_metrics})
+
+        # Validation (check online model and EMA shadow model, use best)
+        val_online = validate_supervised(model, val_loader, criterion, config, device)
+        val_ema = validate_supervised(ema.eval_model(), val_loader, criterion, config, device)
+        val_metrics = val_ema if val_ema["val_auroc"] >= val_online["val_auroc"] else val_online
+        val_history.append({"epoch": epoch, **val_metrics})
+
+        logging.info(
+            f"Ep{epoch} | Loss:{avg_loss:.4f} | "
+            f"Online_AUROC:{val_online['val_auroc']:.4f} | EMA_AUROC:{val_ema['val_auroc']:.4f} | "
+            f"Best_AUROC:{val_metrics['val_auroc']:.4f} | Acc:{val_metrics['val_accuracy']:.4f} | "
+            f"Sens:{val_metrics.get('val_sensitivity', 0):.4f} | "
+            f"LR:{optimizer.param_groups[0]['lr']:.2e}"
+        )
+
+        # Check improvement
+        current_auc = val_metrics["val_auroc"]
+        if current_auc > best_auc:
+            best_auc = current_auc
+            best_epoch = epoch
+            patience_counter = 0
+            save_checkpoint(
+                best_ckpt_path, epoch, "finetune", model, optimizer, scheduler,
+                scaler, ema, best_auc, best_epoch, train_history, val_history,
+                patience_counter, config,
+            )
+            logging.info(f"  ✓ New best AUROC: {best_auc:.4f}")
+        else:
+            patience_counter += 1
+            logging.info(f"  ✗ No improvement ({patience_counter}/{config.patience})")
+
+        # Periodic save
+        if (epoch + 1) % config.save_every == 0:
+            periodic_path = os.path.join(config.checkpoint_dir, f"finetune_epoch{epoch}.pt")
+            save_checkpoint(
+                periodic_path, epoch, "finetune", model, optimizer, scheduler,
+                scaler, ema, best_auc, best_epoch, train_history, val_history,
+                patience_counter, config,
+            )
+
+        # Early stopping
+        if patience_counter >= config.patience:
+            logging.info(f"Early stopping at epoch {epoch}. Best AUROC: {best_auc:.4f} at epoch {best_epoch}")
+            break
+
+        epoch += 1
+        clear_gpu_memory()
+
+    logging.info(f"Training complete. Best AUROC: {best_auc:.4f} at epoch {best_epoch}")
+    return best_ckpt_path
+
+
+def validate_supervised(
+    model: nn.Module,
+    val_loader: DataLoader,
+    criterion: nn.Module,
+    config: Config,
+    device: torch.device,
+) -> Dict[str, float]:
+    """Run validation and compute metrics."""
+    model.eval()
+    all_preds = []
+    all_labels = []
+    total_loss = 0.0
+    count = 0
+
+    with torch.no_grad():
+        for batch in val_loader:
+            sag = batch["sagittal"].to(device)
+            cor = batch["coronal"].to(device)
+            axi = batch["axial"].to(device)
+            sag_m = batch["sag_mask"].to(device)
+            cor_m = batch["cor_mask"].to(device)
+            axi_m = batch["axi_mask"].to(device)
+            labels = batch["label"].to(device)
+
+            with torch.amp.autocast("cuda", enabled=config.use_amp):
+                output = model(sag, cor, axi, sag_m, cor_m, axi_m)
+                logits = output["logits"].squeeze(-1)
+                loss = criterion(logits, labels)
+
+            total_loss += loss.item()
+            all_preds.extend(output["probs"].squeeze(-1).cpu().numpy().tolist())
+            all_labels.extend(labels.cpu().numpy().tolist())
+            count += 1
+
+    metrics = compute_metrics(np.array(all_labels), np.array(all_preds), prefix="val")
+    metrics["val_loss"] = total_loss / max(count, 1)
+    return metrics
+
+
+# ── 5-Fold Cross-Validation Protocol ───────────────────────────────
+
+def train_5fold_cross_validation(
+    config: Config,
+    ssl_checkpoint: Optional[str] = None,
+    task: str = "acl",
+) -> Dict[str, Any]:
+    """
+    Execute patient-stratified 5-Fold Cross-Validation for ACL-LKNet.
+    
+    Partitions the dataset into 5 balanced folds, trains a separate model on each fold,
+    evaluates out-of-fold predictions, and reports Mean ± Std across folds for all
+    academic metrics (AUROC, Accuracy, Sensitivity, Specificity, F1, MCC).
+    
+    Returns:
+        Dict with fold-by-fold results, aggregated Mean ± Std, and out-of-fold metrics.
+    """
+    from .dataset import get_stratified_folds
+    import json
+
+    setup_logging(config.log_dir)
+    logging.info(f"=== Starting Stratified {config.n_splits}-Fold Cross-Validation ===")
+    
+    folds = get_stratified_folds(
+        config.data_dir, split="train", n_splits=config.n_splits, seed=config.seed, task=task
+    )
+
+    base_exp_name = config.experiment_name
+    fold_summaries = []
+    all_oof_labels = []
+    all_oof_preds = []
+
+    for fold_info in folds:
+        fold_idx = fold_info["fold"]
+        logging.info(f"\n--- Running Fold {fold_idx + 1} / {config.n_splits} ---")
+        logging.info(f"Train Cases: {len(fold_info['train_cases'])} | Val Cases: {len(fold_info['val_cases'])}")
+
+        # Create fold-specific configuration
+        fold_config = copy.deepcopy(config)
+        fold_config.experiment_name = f"{base_exp_name}_fold{fold_idx}"
+        fold_config.__post_init__()
+        os.makedirs(fold_config.checkpoint_dir, exist_ok=True)
+        os.makedirs(fold_config.log_dir, exist_ok=True)
+
+        # Train supervised on this fold
+        best_ckpt = train_supervised(
+            fold_config,
+            ssl_checkpoint=ssl_checkpoint,
+            train_cases=fold_info["train_cases"],
+            val_cases=fold_info["val_cases"],
+            train_split="train",
+            val_split="train",
+        )
+
+        # Evaluate best model on fold validation set
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        model = create_model_from_config(fold_config).to(device)
+        load_checkpoint(best_ckpt, model)
+        model.eval()
+
+        _, fold_val_loader = create_dataloaders(
+            fold_config, ssl=False,
+            val_cases=fold_info["val_cases"],
+            val_split="train",
+        )
+
+        fold_preds = []
+        fold_labels = []
+        with torch.no_grad():
+            for batch in fold_val_loader:
+                sag = batch["sagittal"].to(device)
+                cor = batch["coronal"].to(device)
+                axi = batch["axial"].to(device)
+                sag_m = batch["sag_mask"].to(device)
+                cor_m = batch["cor_mask"].to(device)
+                axi_m = batch["axi_mask"].to(device)
+
+                with torch.amp.autocast("cuda", enabled=fold_config.use_amp and torch.cuda.is_available()):
+                    out = model(sag, cor, axi, sag_m, cor_m, axi_m)
+
+                fold_preds.extend(out["probs"].squeeze(-1).cpu().numpy().tolist())
+                fold_labels.extend(batch["label"].numpy().tolist())
+
+        fold_m = compute_metrics(np.array(fold_labels), np.array(fold_preds))
+        fold_summaries.append({
+            "fold": fold_idx + 1,
+            "best_checkpoint": best_ckpt,
+            "accuracy": fold_m["accuracy"],
+            "balanced_accuracy": fold_m["balanced_accuracy"],
+            "auroc": fold_m["auroc"],
+            "auprc": fold_m["auprc"],
+            "sensitivity": fold_m["sensitivity"],
+            "specificity": fold_m["specificity"],
+            "f1": fold_m["f1"],
+            "mcc": fold_m["mcc"],
+        })
+
+        all_oof_labels.extend(fold_labels)
+        all_oof_preds.extend(fold_preds)
+
+    # Compute Mean and Standard Deviation across folds
+    metrics_to_agg = ["accuracy", "balanced_accuracy", "auroc", "auprc", "sensitivity", "specificity", "f1", "mcc"]
+    aggregated = {}
+    for m in metrics_to_agg:
+        vals = [f[m] for f in fold_summaries]
+        aggregated[m] = {
+            "mean": float(np.mean(vals)),
+            "std": float(np.std(vals)),
+            "formatted": f"{np.mean(vals):.4f} ± {np.std(vals):.4f}",
+        }
+
+    # Compute Pooled Out-Of-Fold (OOF) metrics
+    oof_y = np.array(all_oof_labels)
+    oof_p = np.array(all_oof_preds)
+    oof_metrics = compute_metrics(oof_y, oof_p)
+
+    cv_results = {
+        "n_splits": config.n_splits,
+        "fold_results": fold_summaries,
+        "mean_std_summary": aggregated,
+        "pooled_oof_metrics": oof_metrics,
+    }
+
+    # Log and Save CV Summary Report
+    logging.info("\n" + "=" * 75)
+    logging.info(f"=== {config.n_splits}-FOLD CROSS-VALIDATION SUMMARY RESULTS ===")
+    logging.info("-" * 75)
+    logging.info(f"{'Metric':<25} {'Mean ± Std Across Folds':<30} {'Pooled OOF':<15}")
+    logging.info("-" * 75)
+    for m in metrics_to_agg:
+        logging.info(f"{m:<25} {aggregated[m]['formatted']:<30} {oof_metrics[m]:.4f}")
+    logging.info("=" * 75 + "\n")
+
+    results_dir = os.path.join(config.drive_dir, "results")
+    os.makedirs(results_dir, exist_ok=True)
+    with open(os.path.join(results_dir, "5fold_cv_summary.json"), "w") as f:
+        json.dump(cv_results, f, indent=2)
+
+    # Export formatted Markdown summary table
+    md_lines = [
+        f"# {config.n_splits}-Fold Stratified Cross-Validation Results",
+        "",
+        f"**Model Backbone:** `{config.backbone}` | **Dataset:** MRNet ACL | **Folds:** {config.n_splits}",
+        "",
+        "| Metric | Mean ± Std Across Folds | Pooled Out-of-Fold | Fold 1 | Fold 2 | Fold 3 | Fold 4 | Fold 5 |",
+        "| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |",
+    ]
+    for m in metrics_to_agg:
+        f_vals = " | ".join([f"{f[m]:.4f}" for f in fold_summaries])
+        md_lines.append(f"| **{m.replace('_', ' ').title()}** | `{aggregated[m]['formatted']}` | `{oof_metrics[m]:.4f}` | {f_vals} |")
+
+    with open(os.path.join(results_dir, "5fold_cv_summary.md"), "w") as f:
+        f.write("\n".join(md_lines) + "\n")
+
+    return cv_results
+

src/utils.py

@@ -0,0 +1,247 @@
+"""
+Utility functions for ACL-LKNet.
+
+Includes: reproducibility seeding, EMA model, checkpoint save/load,
+logging helpers, and Google Drive integration.
+"""
+
+import os
+import copy
+import random
+import logging
+from typing import Dict, Any, Optional
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+
+# ── Reproducibility ─────────────────────────────────────────────────
+
+def set_seed(seed: int = 42):
+    """Set all random seeds for reproducibility."""
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    os.environ["PYTHONHASHSEED"] = str(seed)
+
+
+def get_rng_states() -> Dict[str, Any]:
+    """Capture all RNG states for exact checkpoint reproducibility."""
+    states = {
+        "python": random.getstate(),
+        "numpy": np.random.get_state(),
+        "torch": torch.get_rng_state(),
+    }
+    if torch.cuda.is_available():
+        states["cuda"] = torch.cuda.get_rng_state_all()
+    return states
+
+
+def set_rng_states(states: Dict[str, Any]):
+    """Restore RNG states from checkpoint."""
+    random.setstate(states["python"])
+    np.random.set_state(states["numpy"])
+    torch.set_rng_state(states["torch"])
+    if "cuda" in states and torch.cuda.is_available():
+        torch.cuda.set_rng_state_all(states["cuda"])
+
+
+# ── Exponential Moving Average ──────────────────────────────────────
+
+class EMAModel:
+    """
+    Exponential Moving Average of model parameters.
+    
+    Maintains a shadow copy of model weights that is updated as:
+        shadow = decay * shadow + (1 - decay) * current
+    
+    Use the EMA model for evaluation — it typically generalizes better.
+    """
+
+    def __init__(self, model: nn.Module, decay: float = 0.999):
+        self.decay = decay
+        self.shadow = copy.deepcopy(model)
+        self.shadow.eval()
+        for p in self.shadow.parameters():
+            p.requires_grad_(False)
+
+    @torch.no_grad()
+    def update(self, model: nn.Module):
+        """Update shadow weights with current model weights and buffers."""
+        for s_param, m_param in zip(self.shadow.parameters(), model.parameters()):
+            s_param.data.mul_(self.decay).add_(m_param.data, alpha=1.0 - self.decay)
+        # Sync BatchNorm running statistics (buffers are not EMA-averaged,
+        # they should directly mirror the training model's batch statistics)
+        for s_buf, m_buf in zip(self.shadow.buffers(), model.buffers()):
+            s_buf.data.copy_(m_buf.data)
+
+    def state_dict(self):
+        return self.shadow.state_dict()
+
+    def load_state_dict(self, state_dict):
+        self.shadow.load_state_dict(state_dict)
+
+    def eval_model(self) -> nn.Module:
+        """Return the shadow model for evaluation."""
+        return self.shadow
+
+
+# ── Checkpoint Management ───────────────────────────────────────────
+
+def save_checkpoint(
+    path: str,
+    epoch: int,
+    phase: str,
+    model: nn.Module,
+    optimizer: torch.optim.Optimizer,
+    scheduler: Any,
+    scaler: Optional[torch.amp.GradScaler],
+    ema: Optional[EMAModel],
+    best_metric: float,
+    best_epoch: int,
+    train_history: list,
+    val_history: list,
+    patience_counter: int,
+    config: Any,
+):
+    """
+    Save a full training checkpoint to Google Drive.
+    
+    Captures everything needed to resume training exactly:
+    model, optimizer, scheduler, AMP scaler, EMA, RNG states, histories.
+    """
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+
+    checkpoint = {
+        "epoch": epoch,
+        "phase": phase,
+        "model_state_dict": model.state_dict(),
+        "optimizer_state_dict": optimizer.state_dict(),
+        "scheduler_state_dict": scheduler.state_dict() if scheduler else None,
+        "scaler_state_dict": scaler.state_dict() if scaler else None,
+        "ema_state_dict": ema.state_dict() if ema else None,
+        "best_metric": best_metric,
+        "best_epoch": best_epoch,
+        "train_history": train_history,
+        "val_history": val_history,
+        "patience_counter": patience_counter,
+        "rng_states": get_rng_states(),
+        "config": config.to_dict() if hasattr(config, "to_dict") else str(config),
+    }
+    torch.save(checkpoint, path)
+    logging.info(f"Checkpoint saved: {path}")
+
+
+def load_checkpoint(
+    path: str,
+    model: nn.Module,
+    optimizer: Optional[torch.optim.Optimizer] = None,
+    scheduler: Any = None,
+    scaler: Optional[torch.amp.GradScaler] = None,
+    ema: Optional[EMAModel] = None,
+) -> Dict[str, Any]:
+    """
+    Load a checkpoint and restore all training state.
+    
+    Returns the checkpoint dict for extracting histories, epoch, etc.
+    """
+    checkpoint = torch.load(path, map_location="cpu", weights_only=False)
+
+    model.load_state_dict(checkpoint["model_state_dict"])
+    if optimizer and "optimizer_state_dict" in checkpoint:
+        optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
+    if scheduler and checkpoint.get("scheduler_state_dict"):
+        scheduler.load_state_dict(checkpoint["scheduler_state_dict"])
+    if scaler and checkpoint.get("scaler_state_dict"):
+        scaler.load_state_dict(checkpoint["scaler_state_dict"])
+    if ema and checkpoint.get("ema_state_dict"):
+        ema.load_state_dict(checkpoint["ema_state_dict"])
+    if "rng_states" in checkpoint:
+        set_rng_states(checkpoint["rng_states"])
+
+    logging.info(f"Checkpoint loaded: {path} (epoch {checkpoint['epoch']})")
+    return checkpoint
+
+
+def find_latest_checkpoint(checkpoint_dir: str, phase: str = "finetune") -> Optional[str]:
+    """Find the latest checkpoint file in the checkpoint directory."""
+    if not os.path.exists(checkpoint_dir):
+        return None
+    
+    checkpoints = [
+        f for f in os.listdir(checkpoint_dir)
+        if f.startswith(f"{phase}_") and f.endswith(".pt")
+    ]
+    if not checkpoints:
+        return None
+
+    # Sort by epoch number
+    def extract_epoch(fname):
+        try:
+            parts = fname.replace(".pt", "").split("_epoch")
+            return int(parts[-1])
+        except (ValueError, IndexError):
+            return -1
+
+    checkpoints.sort(key=extract_epoch)
+    return os.path.join(checkpoint_dir, checkpoints[-1])
+
+
+# ── Logging ─────────────────────────────────────────────────────────
+
+def setup_logging(log_dir: Optional[str] = None, level=logging.INFO):
+    """Configure logging to console and optionally to file."""
+    handlers = [logging.StreamHandler()]
+    if log_dir:
+        os.makedirs(log_dir, exist_ok=True)
+        handlers.append(logging.FileHandler(os.path.join(log_dir, "training.log")))
+
+    logging.basicConfig(
+        level=level,
+        format="%(asctime)s [%(levelname)s] %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+        handlers=handlers,
+        force=True,
+    )
+
+
+# ── Metrics Formatting ─────────────────────────────────────────────
+
+def format_metrics(metrics: Dict[str, float]) -> str:
+    """Format a metrics dict into a readable string."""
+    parts = []
+    for k, v in metrics.items():
+        if isinstance(v, float):
+            parts.append(f"{k}: {v:.4f}")
+        else:
+            parts.append(f"{k}: {v}")
+    return " | ".join(parts)
+
+
+# ── Memory Utils ────────────────────────────────────────────────────
+
+def get_gpu_memory_info() -> Dict[str, float]:
+    """Get GPU memory usage in MB."""
+    if not torch.cuda.is_available():
+        return {"allocated_mb": 0, "reserved_mb": 0, "total_mb": 0}
+    try:
+        props = torch.cuda.get_device_properties(0)
+        total = getattr(props, "total_memory", getattr(props, "total_mem", 0)) / 1024**2
+        return {
+            "allocated_mb": torch.cuda.memory_allocated() / 1024**2,
+            "reserved_mb": torch.cuda.memory_reserved() / 1024**2,
+            "total_mb": total,
+        }
+    except Exception:
+        return {"allocated_mb": 0, "reserved_mb": 0, "total_mb": 0}
+
+
+def clear_gpu_memory():
+    """Force GPU memory cleanup."""
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+        torch.cuda.synchronize()

train_cv.py

@@ -0,0 +1,140 @@
+#!/usr/bin/env python3
+"""
+ACL-LKNet Training CLI
+======================
+Standalone command-line entrypoint for training ACL-LKNet models.
+Supports single-fold training, full 5-fold stratified cross-validation,
+and Phase 1 self-supervised pretraining (Masked Slice Modeling).
+
+Usage Examples:
+    # Train Fold 1 with default configuration:
+    python train_cv.py --data_dir /path/to/mrnet --fold 1
+
+    # Train all 5 folds sequentially for cross-validation:
+    python train_cv.py --data_dir /path/to/mrnet --cv
+
+    # Run Phase 1 Masked Slice Modeling (SSL) pretraining:
+    python train_cv.py --data_dir /path/to/mrnet --ssl_pretrain
+"""
+
+import os
+import sys
+import argparse
+import logging
+import torch
+
+# Ensure local package imports work seamlessly
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from src.config import Config
+from src.train import train_supervised, train_ssl, train_5fold_cross_validation
+from src.utils import set_seed, setup_logging
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Train ACL-LKNet (Phase 1 SSL Pretraining or Phase 2 Supervised Fine-Tuning)."
+    )
+    parser.add_argument(
+        "--data_dir", type=str, default="./data/mrnet",
+        help="Path to Stanford MRNet dataset root directory (containing train, valid, and test folders)."
+    )
+    parser.add_argument(
+        "--output_dir", type=str, default="./checkpoints",
+        help="Directory where model checkpoints and logs will be saved."
+    )
+    parser.add_argument(
+        "--backbone", type=str, default="convnext_tiny",
+        choices=["convnext_tiny", "resnet18", "replknet"],
+        help="Backbone architecture (ConvNeXt-Tiny recommended for large receptive field)."
+    )
+    parser.add_argument(
+        "--fold", type=int, default=1, choices=[1, 2, 3, 4, 5],
+        help="Fold index to train (1-5) when not running full cross-validation."
+    )
+    parser.add_argument(
+        "--cv", action="store_true",
+        help="Run complete 5-fold stratified cross-validation."
+    )
+    parser.add_argument(
+        "--ssl_pretrain", action="store_true",
+        help="Run Phase 1 Masked Slice Modeling (MSM) self-supervised pretraining."
+    )
+    parser.add_argument(
+        "--ssl_checkpoint", type=str, default=None,
+        help="Optional path to SSL-pretrained backbone checkpoint to initialize Phase 2 training."
+    )
+    parser.add_argument(
+        "--epochs", type=int, default=30,
+        help="Maximum training epochs (monitored by early stopping patience=20)."
+    )
+    parser.add_argument(
+        "--lr_backbone", type=float, default=1.0e-5,
+        help="Differential learning rate for pretrained backbone."
+    )
+    parser.add_argument(
+        "--lr_head", type=float, default=3.0e-4,
+        help="Learning rate for newly initialized attention and classification heads."
+    )
+    parser.add_argument(
+        "--batch_size", type=int, default=1,
+        help="Physical batch size (keep = 1 on 15-16 GB GPUs to prevent OOM)."
+    )
+    parser.add_argument(
+        "--accum_steps", type=int, default=8,
+        help="Gradient accumulation steps to achieve effective batch size = 8."
+    )
+    parser.add_argument(
+        "--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu",
+        help="Compute device ('cuda' or 'cpu')."
+    )
+    parser.add_argument(
+        "--seed", type=int, default=42,
+        help="Random seed for reproducibility across PyTorch, NumPy, and Python."
+    )
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    
+    # Initialize master configuration
+    config = Config(
+        data_dir=args.data_dir,
+        checkpoint_dir=args.output_dir,
+        log_dir=os.path.join(args.output_dir, "logs"),
+        backbone_name=args.backbone,
+        epochs=args.epochs,
+        backbone_lr=args.lr_backbone,
+        lr=args.lr_head,
+        batch_size=args.batch_size,
+        accumulation_steps=args.accum_steps,
+        device=args.device,
+        seed=args.seed,
+    )
+    
+    os.makedirs(config.checkpoint_dir, exist_ok=True)
+    os.makedirs(config.log_dir, exist_ok=True)
+    setup_logging(config.log_dir)
+    set_seed(config.seed)
+    
+    logging.info(f"Initialized ACL-LKNet with Backbone: {config.backbone_name} on {config.device}")
+    
+    if args.ssl_pretrain:
+        logging.info("Starting Phase 1: Masked Slice Modeling (MSM) SSL Pretraining...")
+        train_ssl(config)
+    elif args.cv:
+        logging.info(f"Starting 5-Fold Stratified Cross-Validation on MRNet...")
+        results = train_5fold_cross_validation(config, ssl_checkpoint=args.ssl_checkpoint)
+        print("\n" + "=" * 60)
+        print("Cross-Validation Complete! Summary:")
+        print(f"Mean Val AUROC: {results.get('mean_val_auc', 'N/A')}")
+        print("=" * 60)
+    else:
+        logging.info(f"Starting Single Fold Training: Fold {args.fold}...")
+        config.experiment_name = f"acl_lknet_fold{args.fold}"
+        train_supervised(config, fold_idx=args.fold - 1, ssl_checkpoint=args.ssl_checkpoint)
+
+
+if __name__ == "__main__":
+    main()