---
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) [https://mde-lab.aegean.gr/index.php/downloads/]
- **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:  19 December 2025*