Upload multimodal cervical cancer classifier

README.md

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+---
+language:
+  - en
+license: mit
+datasets:
+  - smear2005
+tags:
+  - medical-imaging
+  - multimodal
+  - vision-transformer
+  - cervical-cancer
+  - histopathology
+---
+
+# Cervical Cancer Multimodal Classifier
+
+## Model Description
+
+This is an advanced **multimodal** model that classifies cervical cancer using both:
+- **Visual features** from histopathological images (Vision Transformer)
+- **Morphological features** from tabular data (20 hand-crafted features)
+
+### Model Architecture
+
+```
+┌─────────────────┐         ┌──────────────────┐
+│     Histopath.     │         │ Tabular Features    │
+│     Image (BMP)    │         │   (20 features)     │
+└────────┬────────┘         └────────┬─────────┘
+          │                               │
+          │                               │
+          ▼                              ▼
+   ┌──────────────┐           ┌────────────┐
+   │    ViT-base    │           │    MLP       │
+   │    (768 dims)  │           │    (64 dims) │
+   └──────┬───────┘           └────┬───────┘
+           │                           │
+           └────────┬──────────────┘
+                     │
+                     ▼
+            ┌─────────────────┐
+            │     Fusion Layer   │
+            │     (512 -> 256)   │
+            └────────┬────────┘
+                      │
+                      ▼
+            ┌─────────────────┐
+            │     Output (7)     │
+            │     Classes        │
+            └─────────────────┘
+```
+
+## Supported Classes
+
+1. **carcinoma_in_situ** - Carcinoma in situ
+2. **light_dysplastic** - Light dysplastic
+3. **moderate_dysplastic** - Moderate dysplastic
+4. **normal_columnar** - Normal columnar
+5. **normal_intermediate** - Normal intermediate
+6. **normal_superficiel** - Normal superficial
+7. **severe_dysplastic** - Severe dysplastic
+
+## Performance
+
+|          Metric        |           Value          |
+|------------------------|--------------------------|
+|   **Test Accuracy**    |      0.6594      |
+|   **Test F1-Score**    |      0.6571       |
+| **Weighted Precision** | 0.6558 |
+
+## Training Details
+
+- **Dataset**: Smear2005 (Herlev Colposcopy)
+- **Vision Backbone**: google/vit-base-patch16-224
+- **Training Epochs**: 50 (with early stopping at 10)
+- **Batch Size**: 16
+- **Learning Rate**: 2e-5 (AdamW)
+- **Scheduler**: CosineAnnealingLR
+- **Hardware**: NVIDIA T4 GPU on Google Colab
+
+num_epochs = 50
+best_val_accuracy = 0.6376811594202898
+patience = 10
+patience_counter = 10
+
+## Tabular Features
+
+The model uses 20 morphological features extracted from nuclei analysis:
+
+- **Nucleus Area**: Kerne_A
+- **Cytoplasm Area**: Cyto_A
+- **Nucleus-Cytoplasm Ratio**: K/C
+- **Y-coordinates**: Kerne_Ycol, Cyto_Ycol
+- **Morphological indices**: KerneShort, KerneLong, KerneElong, KerneRund
+- **Perimeter**: KernePeri, CytoPeri
+- **Size ratios**: KerneMax, KerneMin, CytoMax, CytoMin
+- **Position**: KernePos
+
+Features are **StandardScaler normalized** using training set statistics.
+
+## Usage
+
+### Installation
+
+```bash
+pip install torch transformers pillow scikit-learn
+```
+
+### Quick Start
+
+```python
+import torch
+from PIL import Image
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+
+# Load model
+model = torch.load('multimodal_cervical_model.pt')
+
+# Your image and tabular data
+image = Image.open('sample.BMP')
+tabular_features = {
+    'Kerne_A': 803.5,
+    'Cyto_A': 27804.125,
+    # ... 18 more features
+}
+
+# Predict
+predictions = predict_multimodal(image, tabular_features, ...)
+```
+
+## Advantages
+
+✅ **Multimodal Fusion**: Combines spatial-visual features with quantitative morphological data  
+✅ **Robustness**: Less prone to overfitting than single-modality models  
+✅ **Interpretability**: Features are human-interpretable (sizes, ratios, etc.)  
+✅ **Scalability**: Can add more modalities (ultrasound, genetic data, etc.)  
+
+## Limitations
+
+⚠️ Limited to 7 classes (specific dataset)  
+⚠️ Requires both image and tabular data for inference  
+⚠️ Image input must be histopathological cervical samples  
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{cervical_multimodal_2025,
+  title = {Cervical Cancer Multimodal Classifier},
+  author = {Sastelvio MANUEL},
+  year = 2025,
+  howpublished = {\url{https://huggingface.co/sastelvio/cervical-cancer-multimodal-vit}}
+}
+```
+
+## Disclaimer
+
+⚠️ **Medical Use Only Under Professional Supervision**
+
+This model is for research and educational purposes. It should **NOT** be used for clinical diagnosis without:
+- Validation by medical professionals
+- Proper regulatory approval
+- Thorough clinical testing
+- Integration with clinical workflows
+
+## Author
+
+[Sastelvio MANUEL]  
+Portfolio: [https://github.com/sastelvio]
+
+## License
+
+MIT License - See LICENSE file for details
+
+---
+
+*Last updated:  18 December 2025*

config.json

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+{
+  "model_type": "multimodal",
+  "vision_backbone": "google/vit-base-patch16-224",
+  "num_classes": 7,
+  "num_tabular_features": 20,
+  "class_labels": {
+    "0": "carcinoma_in_situ",
+    "1": "light_dysplastic",
+    "2": "moderate_dysplastic",
+    "3": "normal_columnar",
+    "4": "normal_intermediate",
+    "5": "normal_superficiel",
+    "6": "severe_dysplastic"
+  },
+  "feature_columns": [
+    "Kerne_A",
+    "Cyto_A",
+    "K/C",
+    "Kerne_Ycol",
+    "Cyto_Ycol",
+    "KerneShort",
+    "KerneLong",
+    "KerneElong",
+    "KerneRund",
+    "CytoShort",
+    "CytoLong",
+    "CytoElong",
+    "CytoRund",
+    "KernePeri",
+    "CytoPeri",
+    "KernePos",
+    "KerneMax",
+    "KerneMin",
+    "CytoMax",
+    "CytoMin"
+  ],
+  "training_config": {
+    "batch_size": 16,
+    "learning_rate": 2e-05,
+    "num_epochs": 50,
+    "optimizer": "AdamW",
+    "scheduler": "CosineAnnealingLR",
+    "early_stopping_patience": 10
+  }
+}

final_multimodal_model_v1.0.0_20251218.pt

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+size 346793317

requirements.txt

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+torch==2.9.1
+transformers==4.57.3
+huggingface_hub==0.36.0
+datasets==4.4.1
+accelerate==1.12.0
+evaluate==0.4.6
+pillow==12.0.0
+numpy==2.0.2
+pandas==2.2.2
+scikit-learn==1.6.1
+openpyxl==3.1.5
+matplotlib==3.10.0
+seaborn==0.13.2
+wandb==0.23.1