The Prostate cANcer graDe Assessment (PANDA) challenge comprises over 10K whole-slide images (WSIs) of digitized hematoxylin and eosin-stained biopsies originating from Radboud University Medical Center and Karolinska Institute. PANDA-MIL collects the eosin-stained biopsies with region-based masks from Karolinska Institute, indicating the benign (normal) and cancerous (abnormal) tissue, combined by stroma and epithelium. To fit the MIL-based task, we non-overlapped partition each WSI with the highest-level resolution into patches and only keep those patches comprising tissue over the 50% patch size. Each kept patch gets its patch-level annotations from PANDA, and a WSI comprising any abnormal patch is treated as an abnormal WSI. In sum, PANDA-MIL's training split contains 3925 instances with WSI-level annotations, and the testing split includes 975 instances with patch-level annotations.
The PANDA-MIL can be found in this link.
We hope the codebase is beneficial to you. If this repo works positively for your research, please consider citing our paper. Thank you for your time and consideration.
@inproceedings{wu2023Contrastive,
author = {Jhih-Ciang Wu, Ding-Jie Chen and Chiou-Shann Fuh},
title = {Contrastive Feature Decoupling for Weakly-supervised Disease Detection},
booktitle = {MICCAI},
year = {2023},
}
@article{bulten2022artificial,
title={Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge},
author={Bulten, Wouter and Kartasalo, Kimmo and Chen, Po-Hsuan Cameron and Str{\"o}m, Peter and Pinckaers, Hans and Nagpal, Kunal and Cai, Yuannan and Steiner, David F and van Boven, Hester and Vink, Robert and others},
journal={Nature medicine},
volume={28},
number={1},
pages={154--163},
year={2022},
publisher={Nature Publishing Group US New York}
}