MoPaDi combines Diffusion Autoencoders with multiple instance learning (MIL) for explainability of deep learning classifiers in histopathology. This repository contains the supplementary material for the following preprint:
Žigutytė, L., Lenz, T., Han, T., Hewitt, K. J., Reitsam, N. G., Foersch, S., ... & Kather, J. N. (2024). Counterfactual Diffusion Models for Mechanistic Explainability of Artificial Intelligence Models in Pathology. bioRxiv, 2024.
For segmentation of 6 cell types to quantify changes in original and counterfactual images, DeepCMorph pretrained models were used.
Create a virtual environment, e.g. with conda or mamba, clone the repository, and install required packages:
mamba create -n mopadi python=3.8 -c conda-forge
pip install -r requirements.txt
TBA
- Diagnostic WSI from The Cancer Genome Atlas (TCGA)
- Histology images from uniform tumor regions in TCGA Whole Slide Images (Komura & Ishikawa, 2021)
- 100,000 histological images of human colorectal cancer and healthy tissue (Kather et al., 2018)
This project was built upon a DiffAE (MIT license) repository. We thank the developers for making their code open source.
If you find our work useful for your research or if you use parts of the code please consider citing our preprint:
Žigutytė, L., Lenz, T., Han, T., Hewitt, K. J., Reitsam, N. G., Foersch, S., ... & Kather, J. N. (2024). Counterfactual Diffusion Models for Mechanistic Explainability of Artificial Intelligence Models in Pathology. bioRxiv, 2024.
@misc{zigutyte2024mopadi,
title={ounterfactual Diffusion Models for Mechanistic Explainability of Artificial Intelligence Models in Pathology},
author={Laura Žigutytė and Tim Lenz and Tianyu Han and Katherine Jane Hewitt and Nic Gabriel Reitsam and Sebastian Foersch and Zunamys I Carrero and Michaela Unger and Alexander T Pearson and Daniel Truhn and Jakob Nikolas Kather},
year={2024},
eprint={2024.10.29.620913},
archivePrefix={bioRxiv},
url={https://www.biorxiv.org/content/10.1101/2024.10.29.620913v1},
}