mixmil 0.1.1

Attention-based Multi-instance Mixed Models

MixMIL

Code for the paper: Attention-based Multi-instance Mixed Models

Please raise an issue for questions and bug-reports.

Installation

Install with:

pip install mixmil

alternatively, if you want to include the optional experiment and test dependencies use:

pip install "mixmil[experiments,test]"

or if you want to adapt the code:

git clone https://github.com/AIH-SGML/mixmil.git
cd mixmil
pip install -e ".[experiments,test]"

To enable computations on GPU please follow the installation instructions of PyTorch and PyTorch Scatter. MixMIL works e.g. with PyTorch 2.1.

Experiments

See the notebooks in the experiments folder for examples on how to run the simulation and histopathology experiments.

Histopathology

Install anndata (pip install anndata) to run the notebook.

Download Data

To download the embeddings provided by the DSMIL authors, either:

• Full embeddings: python scripts/dsmil_data_download.py
• PCA reduced embeddings: Google Drive

Citation

@misc{engelmann2023attentionbased,
      title={Attention-based Multi-instance Mixed Models}, 
      author={Jan P. Engelmann and Alessandro Palma and Jakub M. Tomczak and Fabian J Theis and Francesco Paolo Casale},
      year={2023},
      eprint={2311.02455},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}