# ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification

Source: [ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification](<https://www.sota2.com/research/paper/68fa6d5a1e209131cea39640>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:32:23.745Z

Authors: Dawid Rymarczyk, Adam Pardyl, Jaros{\\l}aw Kraus, Aneta Kaczy\\'nska, Marek Skomorowski, Bartosz Zieli\\'nski

Published: 2021-08-24T10:02:31.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2108.10612>)

[PDF](<https://arxiv.org/pdf/2108.10612.pdf>)