Whole Slide Image segmentation with weakly supervised multiple instance learning on TCGA | MICCAI2020 https://arxiv.org/abs/2004.05024
This repository is a software system containing an end-to-end Whole Slide Imaging pre-processing pipeline from The Cancer Genome Atlas download documents, as well as a complete implementation of deep learning tumor segmentation from WSI binary labels as detailed in "Weakly supervised multiple instance learning histopathological tumor segmentation".
Download 6461 tumor maps from The Cancer Genome Atlas
Example of Whole Slide Image tumor segmentation (black background; blue: normal tissue; pink: neoplastic tissue).
This software is entirely written in Python3 and contains two major parts:
Use python3 and install mandatory libraries:
virtualenv -p python3 --system-site-packages venv
source venv/bin/activate
pip install -r requirements.txt
source_folder
python -m code.data_processing.main --gdc gdc_executable_path source_folder
This script first downloads all files in the manifest file, then tiles WSI, extracts tiles of a given magnification, removes background tiles, and finally seeks to extract per-slide binary labels from their name. More information here (in construction).
After data download and pre-processing has been performed, launch the training pipeline using:
python -m code.training --source-slides-folder ./data/preprocessed --alpha 0.1 --beta 0. --max-bag-size 100 --no-download
Many parameters are tunable, see python -m code.training --help
More informations about the training pipeline, including available imaging models here (in construction).
This software is released under the GNU Affero General Public License v3.0 license.
If you use this repo or find it useful, please consider citing:
@inproceedings{lerousseau2020weakly,
title={Weakly supervised multiple instance learning histopathological tumor segmentation},
author={Lerousseau, Marvin and Vakalopoulou, Maria and Classe, Marion and Adam, Julien and Battistella, Enzo and Carr{\'e}, Alexandre and Estienne, Th{\'e}o and Henry, Th{\'e}ophraste and Deutsch, Eric and Paragios, Nikos},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={470--479},
year={2020},
organization={Springer}
}
or
Lerousseau M, Vakalopoulou M, Classe M, Adam J, Battistella E, Carré A, Estienne T, Henry T, Deutsch E, Paragios N. Weakly supervised multiple instance learning histopathological tumor segmentation. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2020 Oct 4 (pp. 470-479). Springer, Cham.