  Multi-instance learning (MIL) is widely used in the computer-aided
interpretation of pathological Whole Slide Images (WSIs) to solve the lack of
pixel-wise or patch-wise annotations. Often, this approach directly applies
"natural image driven" MIL algorithms which overlook the multi-scale (i.e.
pyramidal) nature of WSIs. Off-the-shelf MIL algorithms are typically deployed
on a single-scale of WSIs (e.g., 20x magnification), while human pathologists
usually aggregate the global and local patterns in a multi-scale manner (e.g.,
by zooming in and out between different magnifications). In this study, we
propose a novel cross-scale attention mechanism to explicitly aggregate
inter-scale interactions into a single MIL network for Crohn's Disease (CD),
which is a form of inflammatory bowel disease. The contribution of this paper
is two-fold: (1) a cross-scale attention mechanism is proposed to aggregate
features from different resolutions with multi-scale interaction; and (2)
differential multi-scale attention visualizations are generated to localize
explainable lesion patterns. By training ~250,000 H&E-stained Ascending Colon
(AC) patches from 20 CD patient and 30 healthy control samples at different
scales, our approach achieved a superior Area under the Curve (AUC) score of
0.8924 compared with baseline models. The official implementation is publicly
available at https://github.com/hrlblab/CS-MIL.

Computer Vision and Pattern Recognition;Artificial Intelligence