  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.
