xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology, arXiv, 2024.
Julius Hense*, Mina Jamshidi Idaji*, Oliver Eberle, Thomas Schnake, Jonas Dippel, Laure Ciernik,
Oliver Buchstab, Andreas Mock, Frederick Klauschen, Klaus-Robert Müller
* Equal contribution
Accepted as a poster presentation at NeurIPS2024
Open Review: https://openreview.net/forum?id=Y1fPxGevQj
@inproceedings{
hense2024xmil,
title={x{MIL}: Insightful Explanations for Multiple Instance Learning in Histopathology},
author={Julius Hense and Mina Jamshidi Idaji and Oliver Eberle and Thomas Schnake and Jonas Dippel and Laure Ciernik and Oliver Buchstab and Andreas Mock and Frederick Klauschen and Klaus Robert Muller},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Y1fPxGevQj}
}
Summary: In this study, we revisit MIL through the lens of explainable AI (XAI) and introduce xMIL, a refined framework with more general assumptions. We demonstrate how to obtain improved MIL explanations using layer-wise relevance propagation (LRP) and conduct extensive evaluation experiments on three toy settings and four real-world histopathology datasets.
The two models with their implementation of xMIL-LRP available in this repository are: Attention MIL and TransMIL.
Additionally, you can perform the training on your data with Additive MIL.
The implementation of the models
can be found under the module models.
The script train.py should be used for model training. A template bash script for running train.py
is provided in scripts/train_<model_name>_template.sh with model_name being either attnmil
or transmil. The classifier class for each model is implemented in the respective module.
The training tools can be found under training.
We introduce novel toy experiments for benchmarking explanation methods in complex context-sensitive scenarios.
The related tools and classes are under the module toy_experiments.
The script toy_experiment.py should be used for running experiments.
A template bash script for running experiments is provided in
scripts/toy_experiment_template.sh
The module xai includes the explanation tools.
The class xMIL in xai/explanation.py is the base class for explaining MIL models.
The explanation class for each model is implemented in their respective module under models as x<model_name>,
for example xTransMIL in models/trainsmil.py.
For an explanation model xmodel, the main method to get the explanation scores for a batch is xmodel.get_heatmap(batch).
The notebook notebooks/slide_visualizations_compute_heatmaps.ipynb demonstrates how explanation scores can be computed
for a slide using a model checkpoint.
The script test.py can be used for testing a model checkpoint on a test dataset.
The test results will be saved under the specified results_dir as test_performance.pt and test_performance.csv.
If specified in the input arguments, the explanation scores will be computed and saved in test_prediction.csv.
The script scripts/test_template.sh is a template script for running test.py.
The module visualization/slideshow.py includes the tools for visualizing the slides and heatmaps.
Two notebooks notebooks/slide_visualizations_*.ipynb are provided for demonstrating how to plot the heatmaps.
notebooks/slide_visualizations_precomputed_heatmaps.ipynb shows how to perform the visualization when the explanation
scores are precomputed. If the explanation scores are not precomputed using test.py,
the notebook notebooks/slide_visualizations_compute_heatmaps.ipynb should be used.
The class xMILEval under xai/evaluation.py is the class for patch flipping evaluation experiments.
The script evaluate_patch_flipping.py is used for performing patch flipping experiments.
The bash script scripts/patch_flipping_template.sh is a template of how to run faithfulness experiments
using evaluate_patch_flipping.py.
For reproducibility purposes, we share the training configurations, model parameters, and data splits.
You can download TCGA HNSC, LUAD, and LUSC data from https://www.cancer.gov/tcga. The CAMELYON16 dataset can be downloaded from https://camelyon16.grand-challenge.org/. The HPV status of HNSC dataset and the TP53 mutations of LUAD dataset were downloaded from cBioPortal https://www.cbioportal.org/.
We extracted patches from the slides of 256 × 256 pixels without overlap at 20x magnification (0.5 microns per pixel). We identified and excluded background patches via Otsu’s method on slide thumbnails and applied a patch-level minimum standard deviation of 8. Features were extracted using the pre-trained CTransPath foundation model. The following file structure is required for using our data loader:
- A metadata directory containing
- a file
case_metadata.csvwith one row per case and columns for thecase_idand some prediction target column, and - a file
slide_metadata.csvwith one row per slide and columns for thecase_idand theslide_id.
- a file
- A case-level split created via
split.pyof the aforementionedcase_metadata.csv. - A patches directory containing a folder per slide with patch files and a
metadata/df.csvfile with one row per patch and a columnpatch_ididentifying all patches. - A features directory containing a PyTorch file
{slide_id}.ptper slide, which includes a Tensor of extracted features in the same order as the sortedpatch_idvalues of this slide (ascending). The shape of each Tensor should be(num_patches, num_features).
The data splitting for the experiments in the manuscript was performed using the scripts under scripts/splitting.
The split files are provided under the folder results/splits.
The best hyperparameter configurations as well as the model checkpoints trained and used in this study
are provided under folder results.
The notebook notebooks/patch_flipping_plot_replication.ipynb can be used for replicating the results of patch flipping experiments.
If you face issues using our codes, or you wish to have the implementation of xMIL-LRP for a new model, you can open an issue in this repository, or contact us:
📧 Julius Hense and Mina Jamshidi
If you find our codes useful in your work, please cite us:
@inproceedings{
hense2024xmil,
title={x{MIL}: Insightful Explanations for Multiple Instance Learning in Histopathology},
author={Julius Hense and Mina Jamshidi Idaji and Oliver Eberle and Thomas Schnake and Jonas Dippel and Laure Ciernik and Oliver Buchstab and Andreas Mock and Frederick Klauschen and Klaus Robert Muller},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Y1fPxGevQj}
}
©️ This code is provided under CC BY-NC-ND 4.0. Please refer to the license file for details.
