ProtoDiv: Prototype-guided Division of Consistent Pseudo-bags for Whole-slide Image Classification
This work addresses a domain-specific challenge in medical imaging for pathologists, offering an incremental improvement in pseudo-bag-based multiple instance learning.
The paper tackles the problem of pseudo-bag division for whole-slide image classification by proposing ProtoDiv, a prototype-guided scheme that improves classification performance on two public datasets, with experiments showing obvious performance gains across seven baseline models.
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.