ProtoMIL: Multiple Instance Learning with Prototypical Parts for
Fine-Grained Interpretability
- URL: http://arxiv.org/abs/2108.10612v1
- Date: Tue, 24 Aug 2021 10:02:31 GMT
- Title: ProtoMIL: Multiple Instance Learning with Prototypical Parts for
Fine-Grained Interpretability
- Authors: Dawid Rymarczyk and Aneta Kaczy\'nska and Jaros{\l}aw Kraus and Adam
Pardyl and Bartosz Zieli\'nski
- Abstract summary: Multiple Instance Learning (MIL) gains popularity in many real-life machine learning applications due to its weakly supervised nature.
In this paper, we introduce 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.
- Score: 2.094672430475796
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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