---
license: mit
language:
- en
library_name: pytorch
tags:
- medical-imaging
- mammography
- self-supervised-learning
- byol
- breast-cancer
- computer-vision
- resnet50
pipeline_tag: image-classification
datasets:
- mammogram-breast-tissue-tiles
metrics:
- accuracy
- precision
- recall
- f1
base_model:
- microsoft/resnet-50
---

# BYOL Mammogram Classification Model

A self-supervised learning model for mammogram analysis using Bootstrap Your Own Latent (BYOL) pre-training with ResNet50 backbone.

## Model Description

This model implements BYOL (Bootstrap Your Own Latent) self-supervised pre-training on mammogram breast tissue tiles, followed by fine-tuning for classification tasks. The model is designed specifically for medical imaging applications with aggressive background rejection and intelligent tissue segmentation.

### Key Features

- **Self-supervised pre-training**: Uses BYOL to learn meaningful representations from unlabeled mammogram data
- **Aggressive background rejection**: Multi-level filtering eliminates empty space and background tiles
- **Medical-optimized augmentations**: Preserves anatomical details while providing effective augmentation
- **High-quality tile extraction**: Intelligent breast tissue segmentation with frequency-based selection
- **A100 GPU optimized**: Mixed precision training with advanced optimizations

## Model Architecture

- **Backbone**: ResNet50 (ImageNet pre-trained → BYOL fine-tuned)
- **Input dimension**: 2048 (ResNet50 features)
- **Hidden dimension**: 4096
- **Projection dimension**: 256
- **Tile size**: 512x512 pixels
- **Input format**: RGB (grayscale mammograms converted to RGB)

## Training Details

### BYOL Pre-training
- **Epochs**: 100
- **Batch size**: 32 (A100 optimized)
- **Learning rate**: 2e-3 with warmup
- **Optimizer**: AdamW with cosine annealing
- **Mixed precision**: Enabled for A100 optimization
- **Momentum updates**: Per-step momentum scheduling (0.996 → 1.0)

### Data Processing
- **Tile extraction**: 512x512 pixels with 50% overlap
- **Background rejection**: Multiple criteria including intensity, frequency energy, and tissue ratio
- **Minimum breast ratio**: 15% (increased from typical 30%)
- **Minimum frequency energy**: 0.03 (aggressive threshold)
- **Augmentations**: Medical-safe rotations, flips, color jittering, perspective transforms

## Usage

### Loading the Model

```python
import torch
from train_byol_mammo import MammogramBYOL
from torchvision import models
import torch.nn as nn

# Load the pre-trained BYOL model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Create ResNet50 backbone
resnet = models.resnet50(weights=None)
backbone = nn.Sequential(*list(resnet.children())[:-1])

# Initialize BYOL model
model = MammogramBYOL(
    backbone=backbone,
    input_dim=2048,
    hidden_dim=4096,
    proj_dim=256
).to(device)

# Load pre-trained weights
checkpoint = torch.load('mammogram_byol_best.pth', map_location=device)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
```

### Feature Extraction

```python
# Extract features from mammogram tiles
def extract_features(image_tensor):
    with torch.no_grad():
        features = model.get_features(image_tensor)
    return features

# Example usage
image = torch.randn(1, 3, 512, 512).to(device)  # Example input
features = extract_features(image)  # Returns 2048-dim features
```

### Classification Fine-tuning

Use the provided `train_classification.py` script for downstream classification tasks:

```bash
python train_classification.py \
    --byol_checkpoint ./mammogram_byol_best.pth \
    --train_csv ./train_labels.csv \
    --val_csv ./val_labels.csv \
    --tiles_dir ./tiles/ \
    --output_dir ./classification_results/
```

## File Structure

```
BYOL_Mammogram/
├── mammogram_byol_best.pth          # Best BYOL checkpoint
├── mammogram_byol_final.pth         # Final BYOL checkpoint
├── train_byol_mammo.py              # BYOL pre-training script
├── train_classification.py          # Classification fine-tuning
├── inference_classification.py     # Inference script
├── classification_config.json      # Classification configuration
├── CLASSIFICATION_GUIDE.md         # Detailed training guide
└── requirements.txt                # Dependencies
```

## Performance

### Pre-training Results
- **Dataset**: High-quality breast tissue tiles with aggressive background rejection
- **Efficiency**: ~15-20% tile selection rate (quality over quantity)
- **Background contamination**: 0% (eliminated during extraction)
- **Training time**: ~100 epochs on A100 GPU

### Key Metrics
- **Average breast tissue per tile**: >15%
- **Average frequency energy**: >0.03
- **Tile quality**: Medical-grade with preserved anatomical details

## Technical Specifications

### Hardware Requirements
- **GPU**: A100 (40GB/80GB) recommended
- **Memory**: 35-40GB GPU memory for training
- **CPU**: 16+ cores for data loading

### Dependencies
```
torch>=2.0.0
torchvision>=0.15.0
lightly>=1.4.0
opencv-python>=4.8.0
scipy>=1.10.0
numpy>=1.24.0
Pillow>=9.5.0
tqdm>=4.65.0
```

## Medical Imaging Considerations

### Data Safety
- **Augmentation strategy**: Preserves medical accuracy while providing diversity
- **Background rejection**: Prevents training on non-diagnostic regions
- **Tissue focus**: Emphasizes clinically relevant breast tissue areas

### Clinical Applications
- **Screening support**: Potential for computer-aided detection
- **Research tool**: Feature extraction for medical AI research
- **Educational**: Understanding mammogram image analysis

## Limitations

- **Domain specific**: Trained specifically on mammogram data
- **Preprocessing required**: Requires proper tissue segmentation
- **Computational intensive**: Large model requiring substantial GPU resources
- **Medical supervision**: Requires clinical validation for any medical application

## Citation

If you use this model in your research, please cite:

```bibtex
@model{byol_mammogram_2024,
  title={BYOL Mammogram Classification Model},
  author={PranayPalem},
  year={2024},
  url={https://huggingface.co/PranayPalem/BYOL_Mammogram}
}
```

## License

MIT License - See LICENSE file for details.

## Disclaimer

This model is for research purposes only and should not be used for clinical diagnosis without proper validation and medical supervision. Always consult healthcare professionals for medical decisions.