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ProtoMIL: Multiple Instance Learning with Prototypical Parts for Fine-Grained Interpretability

2021-08-24 10:02:31
Dawid Rymarczyk, Aneta Kaczyńska, Jarosław Kraus, Adam Pardyl, Bartosz Zieliński
arXiv_AI
     
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.

Abstract (translated)

URL

https://arxiv.org/abs/2108.10612

PDF

https://arxiv.org/pdf/2108.10612.pdf

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