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70f7ab5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 | """PyTorch datasets for the three modalities.
All datasets return a uniform batch dict so the trainer is modality-agnostic:
{"slices": (T,3,H,W) | "vol": (1,D,H,W), "tab": (F,), "y": int, "subject_id": str}
Tabular features are standardized with statistics FIT ON THE TRAINING FOLD ONLY
(passed in as `tab_stats`) — never on the full dataset — to prevent leakage.
Missing tabular values are median/mode-imputed from training-fold stats too.
"""
from __future__ import annotations
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset
ROOT = Path(__file__).resolve().parents[3]
PROC2D = ROOT / "data" / "processed_2d"
PROC3D = ROOT / "data" / "processed_3d"
# imagenet stats for pretrained backbones (applied after per-volume z-score,
# so slices are re-scaled into the pretrained input regime)
IMAGENET_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32)
IMAGENET_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32)
def fit_tab_stats(df: pd.DataFrame, features) -> dict:
"""Compute train-fold imputation + standardization stats for tab features."""
stats = {}
for f in features:
col = df[f].astype(float)
med = float(col.median())
vals = col.fillna(med)
mu, sd = float(vals.mean()), float(vals.std())
stats[f] = {"median": med, "mean": mu, "std": sd if sd > 1e-6 else 1.0}
return stats
def _encode_tab(row, features, stats) -> np.ndarray:
out = np.zeros(len(features), dtype=np.float32)
for i, f in enumerate(features):
v = row[f]
s = stats[f]
if pd.isna(v):
v = s["median"]
out[i] = (float(v) - s["mean"]) / s["std"]
return out
class SliceDataset(Dataset):
"""2D / 2.5D tri-planar slices. planes/n_slices select which of the 27 to load.
modality:
slice2d -> single center axial slice returned as (1,3,H,W)
slice25d -> the requested planes x n_slices as (T,3,H,W)
"""
def __init__(self, df, features, tab_stats, planes=("axial",), n_slices=9,
modality="slice25d", augment=False):
self.df = df.reset_index(drop=True)
self.features = features
self.stats = tab_stats
self.planes = planes
self.n_slices = n_slices
self.modality = modality
self.augment = augment
def __len__(self):
return len(self.df)
def _load_plane(self, sid, plane):
arr = np.load(PROC2D / sid / plane / "slices.npy").astype(np.float32) # (9,H,W)
# select n_slices centered subset if fewer requested
if self.n_slices < arr.shape[0]:
start = (arr.shape[0] - self.n_slices) // 2
arr = arr[start:start + self.n_slices]
return arr
def _to_rgb(self, sl): # (H,W) -> (3,H,W) imagenet-normalized
x = np.stack([sl, sl, sl], axis=0)
x = (x - IMAGENET_MEAN[:, None, None]) / IMAGENET_STD[:, None, None]
return x.astype(np.float32)
def _augment(self, arr): # light aug on a (n,H,W) stack
if not self.augment:
return arr
if np.random.rand() < 0.5:
arr = arr + np.random.normal(0, 0.02, arr.shape).astype(np.float32)
if np.random.rand() < 0.5:
arr = arr * np.random.uniform(0.95, 1.05)
return arr
def __getitem__(self, idx):
row = self.df.iloc[idx]
sid = row["subject_id"]
if self.modality == "slice2d":
axial = self._load_plane(sid, "axial")
center = axial[len(axial) // 2]
slices = self._to_rgb(self._augment(center[None]))[0][None] # (1,3,H,W)
else:
planes = []
for p in self.planes:
stack = self._augment(self._load_plane(sid, p))
planes.append(np.stack([self._to_rgb(s) for s in stack])) # (n,3,H,W)
slices = np.concatenate(planes, axis=0) # (T,3,H,W)
return {
"slices": torch.from_numpy(slices),
"tab": torch.from_numpy(_encode_tab(row, self.features, self.stats)),
"y": int(row["class_id"]),
"subject_id": sid,
}
class VolumeDataset(Dataset):
"""Whole-brain 3D volumes for CNN3D / transformer / hybrid models."""
def __init__(self, df, features, tab_stats, target="cnn3d", augment=False):
self.df = df.reset_index(drop=True)
self.features = features
self.stats = tab_stats
self.target = target # cnn3d (128^3) or tf3d (96x112x112)
self.augment = augment
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
row = self.df.iloc[idx]
sid = row["subject_id"]
vol = np.load(PROC3D / self.target / f"{sid}.npy").astype(np.float32)
if self.augment and np.random.rand() < 0.5:
vol = vol + np.random.normal(0, 0.02, vol.shape).astype(np.float32)
return {
"vol": torch.from_numpy(vol[None]), # (1,D,H,W)
"tab": torch.from_numpy(_encode_tab(row, self.features, self.stats)),
"y": int(row["class_id"]),
"subject_id": sid,
}
class TabularDataset(Dataset):
def __init__(self, df, features, tab_stats):
self.df = df.reset_index(drop=True)
self.features = features
self.stats = tab_stats
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
row = self.df.iloc[idx]
return {
"tab": torch.from_numpy(_encode_tab(row, self.features, self.stats)),
"y": int(row["class_id"]),
"subject_id": row["subject_id"],
}
def collate(items):
"""Uniform collate that stacks whichever tensors are present."""
out = {"y": torch.tensor([it["y"] for it in items]),
"subject_id": [it["subject_id"] for it in items]}
for key in ("slices", "vol", "tab"):
if key in items[0]:
out[key] = torch.stack([it[key] for it in items])
return out
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