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Multi-instance learning (MIL) is widely used in the computer-aidedinterpretation of pathological Whole Slide Images (WSIs) to solve the lack ofpixel-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 deployedon a single-scale of WSIs (e.g., 20x magnification), while human pathologistsusually aggregate the global and local patterns in a multi-scale manner (e.g.,by zooming in and out between different magnifications). In this study, wepropose a novel cross-scale attention mechanism to explicitly aggregateinter-scale interactions into a single MIL network for Crohn's Disease (CD),which is a form of inflammatory bowel disease. The contribution of this paperis two-fold: (1) a cross-scale attention mechanism is proposed to aggregatefeatures from different resolutions with multi-scale interaction; and (2)differential multi-scale attention visualizations are generated to localizeexplainable lesion patterns. By training ~250,000 H&E-stained Ascending Colon(AC) patches from 20 CD patient and 30 healthy control samples at differentscales, our approach achieved a superior Area under the Curve (AUC) score of0.8924 compared with baseline models. The official implementation is publiclyavailable at https://github.com/hrlblab/CS-MIL.

Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images