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

TL;DR

ProtoDiv introduces a prototype-guided pseudo-bag division method for weakly labeled whole-slide image classification, enhancing performance without complex architectures by dynamically optimizing attention-based prototypes.

Contribution

The paper proposes a novel, prototype-guided pseudo-bag division scheme for WSI classification that improves performance while maintaining sample consistency.

Findings

ProtoDiv improves classification accuracy across multiple models.

The scheme enhances data augmentation effectiveness.

Performance gains are confirmed on public datasets.

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.