ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging

This repository contains the code for the paper ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging. The paper was accepted at the conference ECAI 2023.

How to use this code

Before using the code prepare the data following the instructions from https://github.com/apardyl/ProtoMIL

Requirements

We use the following packages:

- python=3.10
- pytorch 2.0

Running the code

Run specific main file responsible for a given dataset. The model assumes that the representations for patches from big histology datasets (e.g. TCGA NSCLC) are already extracted using pretrained network.

Citation

If you find this work useful, please cite our paper:

@article{struski2023promil,
  title={ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging},
  author={Struski, {\L}ukasz and Rymarczyk, Dawid and Lewicki, Arkadiusz and Sabiniewicz, Robert and Tabor, Jacek and Zieli{\'n}ski, Bartosz},
  journal={arXiv preprint arXiv:2306.10535},
  year={2023}
}