ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification
Abstract
Multiple Instance Learning (MIL) gains popularity in many real-life machine learning applications due to its weakly supervised nature. However, the corresponding effort on explaining MIL lags behind, and it is usually limited to presenting instances of a bag that are crucial for a particular prediction. In this paper, we fill this gap by introducing ProtoMIL, a novel self-explainable MIL method inspired by the case-based reasoning process that operates on visual prototypes. Thanks to incorporating prototypical features into objects description, ProtoMIL unprecedentedly joins the model accuracy and fine-grained interpretability, which we present with the experiments on five recognized MIL datasets.
Cite
Text
Rymarczyk et al. "ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022. doi:10.1007/978-3-031-26387-3_26Markdown
[Rymarczyk et al. "ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022.](https://mlanthology.org/ecmlpkdd/2022/rymarczyk2022ecmlpkdd-protomil/) doi:10.1007/978-3-031-26387-3_26BibTeX
@inproceedings{rymarczyk2022ecmlpkdd-protomil,
title = {{ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification}},
author = {Rymarczyk, Dawid and Pardyl, Adam and Kraus, Jaroslaw and Kaczynska, Aneta and Skomorowski, Marek and Zielinski, Bartosz},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2022},
pages = {421-436},
doi = {10.1007/978-3-031-26387-3_26},
url = {https://mlanthology.org/ecmlpkdd/2022/rymarczyk2022ecmlpkdd-protomil/}
}