The following repository provides the source code for the experiments presented in the paper "Detection of breast cancer using machine learning on time-series diffuse optical transillumination data" by Nils Harnischmacher, Erik Rodner, and Christoph H. Schmitz. We are applying machine learning algorithms to dynamic optical mammography data, enabling advanced analysis and insights.

Please note that the current documentation of the code is work-in-progress.

The code is written in python and follows the classical workflow of installing python packages. Please note that we tested all of the code on a Linux machine and there is no official support for Windows systems currently

1. Create a virtual environment of your choice - Python 3.10 is recommended
2. Install dependencies:

   pip install -r requirements.txt

3. Install our code as a package

   cd code
   pip install .

The data used in our study is provided at https://osf.io/4cr3z/. Please download the data and unzip it into the folder code\data\downloaded_data. After that, run the data preparation script to rename the files accordingly by:

python code/data/data_preperation.py

If completed successfully, the files in the folder code\data\downloaded_data can be deleted.

All experiments can be run individually. For example the classification results can be reproduced by:

python code/classification_moduls/classification.py

To switch between bilateral and unilateral modes, open the respective experiment's Python script and modify the settings at the bottom of the script. Example:

if __name__ == "__main__":
    mode="uni" # change to "bi" here 

Ensure you modify the mode (bilateral or unilateral) directly within the script before running.

1. code/classification_moduls/classification.py- Experiment for bilateral and unilateral scenarious with a linear model
2. code/classification_moduls/autogluon.py - Experiments using AutoGluon (AutoML)
3. code/sparsification_simulation/sparsification.py- Experiments using a reduced hardware setup (see Section 3.8 of our paper)

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

If you use our code in our research, please cite our paper accordingly.