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.
@article{arxiv.2108.10612,
title = {ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification},
author = {Dawid Rymarczyk and Adam Pardyl and Jarosław Kraus and Aneta Kaczyńska and Marek Skomorowski and Bartosz Zieliński},
journal= {arXiv preprint arXiv:2108.10612},
year = {2022}
}