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Due to the limitations of inadequate Whole-Slide Image (WSI) samples withweak labels, pseudo-bag-based multiple instance learning (MIL) appears as avibrant prospect in WSI classification. However, the pseudo-bag dividingscheme, often crucial for classification performance, is still an open topicworth exploring. Therefore, this paper proposes a novel scheme, ProtoDiv, usinga bag prototype to guide the division of WSI pseudo-bags. Rather than designingcomplex network architecture, this scheme takes a plugin-and-play approach tosafely augment WSI data for effective training while preserving sampleconsistency. Furthermore, we specially devise an attention-based prototype thatcould be optimized dynamically in training to adapt to a classification task.We apply our ProtoDiv scheme on seven baseline models, and then carry out agroup of comparison experiments on two public WSI datasets. Experiments confirmour ProtoDiv could usually bring obvious performance improvements to WSIclassification.

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