  Histopathology image analysis is the golden standard of clinical diagnosis
for Cancers. In doctors daily routine and computer-aided diagnosis, the Whole
Slide Image (WSI) of histopathology tissue is used for analysis. Because of the
extremely large scale of resolution, previous methods generally divide the WSI
into a large number of patches, then aggregate all patches within a WSI by
Multi-Instance Learning (MIL) to make the slide-level prediction when
developing computer-aided diagnosis tools. However, most previous WSI-MIL
models using global-attention without pairwise interaction and any positional
information, or self-attention with absolute position embedding can not well
handle shape varying large WSIs, e.g. testing WSIs after model deployment may
be larger than training WSIs, since the model development set is always limited
due to the difficulty of histopathology WSIs collection. To deal with the
problem, in this paper, we propose to amend position embedding for shape
varying long-contextual WSI by introducing Linear Bias into Attention, and
adapt it from 1-d long sequence into 2-d long-contextual WSI which helps model
extrapolate position embedding to unseen or under-fitted positions. We further
utilize Flash-Attention module to tackle the computational complexity of
Transformer, which also keep full self-attention performance compared to
previous attention approximation work. Our method, Long-contextual MIL
(Long-MIL) are evaluated on extensive experiments including 4 dataset including
WSI classification and survival prediction tasks to validate the superiority on
shape varying WSIs. The source code will be open-accessed soon.
