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.

翻译：针对弱标注全切片图像（WSI）样本存在的不足，基于伪包的多示例学习（MIL）在WSI分类中展现出蓬勃前景。然而，通常对分类性能具有关键影响的伪包划分方案仍是一个值得探索的开放性课题。为此，本文提出一种新颖方案ProtoDiv，利用包原型引导WSI伪包的划分。该方案无需设计复杂网络架构，采用即插即用方法在保持样本一致性的同时安全增强WSI数据以实现有效训练。此外，我们专门设计了一种基于注意力的原型，可在训练过程中动态优化以适应分类任务。我们将ProtoDiv方案应用于七个基线模型，并在两个公开WSI数据集上开展系列对比实验。实验证实，我们的ProtoDiv通常能为WSI分类带来显著性能提升。