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Histopathology image analysis is the golden standard of clinical diagnosisfor Cancers. In doctors daily routine and computer-aided diagnosis, the WholeSlide Image (WSI) of histopathology tissue is used for analysis. Because of theextremely large scale of resolution, previous methods generally divide the WSIinto a large number of patches, then aggregate all patches within a WSI byMulti-Instance Learning (MIL) to make the slide-level prediction whendeveloping computer-aided diagnosis tools. However, most previous WSI-MILmodels using global-attention without pairwise interaction and any positionalinformation, or self-attention with absolute position embedding can not wellhandle shape varying large WSIs, e.g. testing WSIs after model deployment maybe larger than training WSIs, since the model development set is always limiteddue to the difficulty of histopathology WSIs collection. To deal with theproblem, in this paper, we propose to amend position embedding for shapevarying long-contextual WSI by introducing Linear Bias into Attention, andadapt it from 1-d long sequence into 2-d long-contextual WSI which helps modelextrapolate position embedding to unseen or under-fitted positions. We furtherutilize Flash-Attention module to tackle the computational complexity ofTransformer, which also keep full self-attention performance compared toprevious attention approximation work. Our method, Long-contextual MIL(Long-MIL) are evaluated on extensive experiments including 4 dataset includingWSI classification and survival prediction tasks to validate the superiority onshape varying WSIs. The source code will be open-accessed soon.

Long-MIL: Scaling Long Contextual Multiple Instance Learning for Histopathology Whole Slide Image Analysis