ProtoDiv: Prototype-guided Division of Consistent Pseudo-bags for Whole-slide Image Classification

• URL: http://arxiv.org/abs/2304.06652v1
• Date: Thu, 13 Apr 2023 16:27:08 GMT
• Title: ProtoDiv: Prototype-guided Division of Consistent Pseudo-bags for Whole-slide Image Classification
• Authors: Rui Yang, Pei Liu, and Luping Ji
• Abstract summary: Pseudo-bag dividing scheme, often crucial for classification performance, is still an open topic worth exploring. This paper proposes a novel scheme, ProtoDiv, using a bag prototype to guide the division of WSI pseudo-bags.
• Score: 5.836559246348487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the limitations of inadequate Whole-Slide Image (WSI) samples with weak labels, pseudo-bag-based multiple instance learning (MIL) appears as a vibrant prospect in WSI classification. However, the pseudo-bag dividing scheme, often crucial for classification performance, is still an open topic worth exploring. Therefore, this paper proposes a novel scheme, ProtoDiv, using a bag prototype to guide the division of WSI pseudo-bags. Rather than designing complex network architecture, this scheme takes a plugin-and-play approach to safely augment WSI data for effective training while preserving sample consistency. Furthermore, we specially devise an attention-based prototype that could be optimized dynamically in training to adapt to a classification task. We apply our ProtoDiv scheme on seven baseline models, and then carry out a group of comparison experiments on two public WSI datasets. Experiments confirm our ProtoDiv could usually bring obvious performance improvements to WSI classification.

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