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 this https URL.

本文提出了一种新的跨度注意机制来聚合具有不同分辨率的图像中的特征，并生成不同分辨率的可视化结果来定位并解释可解释的损伤模式，该方法在 Crohn 病这种炎症性肠病的诊断中取得了优异的成果。