Breast Cancer Subtype Classification

This project implements deep learning (CNNs) and gradient-boosted tree models to identify breast cancer subtypes from medical imaging data.

Project Overview

The project aims to:

• Classify breast cancer subtypes using medical imaging data
• Compare performance between CNNs and gradient-boosted tree models
• Provide interpretable results through explainable AI techniques

Project Structure

.
├── data/                    # Data directory (not tracked in git)
│   ├── raw/                # Raw input data
│   └── processed/          # Processed data
├── notebooks/              # Jupyter notebooks
│   └── data_requirements.ipynb  # Documentation of required data
├── src/                    # Source code
│   ├── __init__.py
│   ├── data/              # Data processing scripts
│   ├── models/            # Model implementations
│   └── utils/             # Utility functions
├── requirements.txt        # Python dependencies
└── README.md              # This file

Setup

1. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

Required Data

See notebooks/data_requirements.ipynb for detailed information about:

• Required data formats
• Data preprocessing steps
• Data sources and acquisition

Models

The project implements two main approaches:

1. Convolutional Neural Networks (CNNs)

   • Architecture optimized for medical imaging
   • Transfer learning from pre-trained models

2. Gradient Boosted Trees

   • XGBoost/LightGBM implementation
   • Feature engineering pipeline

Contributing

Please read the data requirements notebook before contributing to ensure all necessary data formats and preprocessing steps are followed.

License

MIT License