Breast Cancer Detection via Multi-Tiered Self-Contrastive Learning in Microwave Radiometric Imaging

Setup

To set up the project, follow these steps:

1. Install Anaconda from the official website: Anaconda.

2. Clone the repository to your local machine:

   git clone https://github.com/cgalaz01/self_contrastive_mwr.git
   

3. Navigate to the project directory:

   cd self_contrastive_mwr
   

4. Create a new conda environment using the provided environment.yml file:

   conda env create -f environment.yml
   

5. Activate the conda environment:

   conda activate self_contrastive_mwr
   

Model Training

To train and evaluate a model run the Python script 'run_trianing.py':

1. Navigate to the project's source code.

   cd src
   

2. Run the Python script with the desired command-line arguments. For example, to run the script with the default values for model_type and contrastive_type, use the following command:

   python run_training.py
   

3. If you want to specify different values for the arguments, use the --model_type (either 'base', 'local', 'regional', 'global' or 'joint') and --contrastive_type (either 'none', 'contrastive', 'triplethard', 'tripletsemihard' or 'npairs') flags followed by the desired values. Note: 'joint' model expects the respective 'local', 'regional' and 'global' models to be trained first. For example:

   python run_training.py --model_type local --contrastive_type none
   

Contributing

Contributions are welcome! Here's how you can contribute to the project:

1. Fork the repository.
2. Create a new branch: git checkout -b feature/your-feature-name.
3. Make your changes and commit them: git commit -m 'Add some feature'.
4. Push to the branch: git push origin feature/your-feature-name.
5. Open a pull request.

Citation

If you found this code useful for your project please cite as:

@article{galazis2025breast,
  title={Breast Cancer Detection via Multi-Tiered Self-Contrastive Learning in Microwave Radiometric Imaging},
  author={Galazis, Christoforos and Wu, Huiyi and Goryanin, Igor},
  journal={Diagnostics},
  volume={15},
  number={5},
  pages={549},
  year={2025},
  publisher={MDPI}
}

License

This project is licensed under the MIT License. See the LICENSE file for more information.