Analyzing high resolution whole slide images (WSIs) with regard to
information across multiple scales poses a significant challenge in digital
pathology. Multi-instance learning (MIL) is a common solution for working with
high resolution images by classifying bags of objects (i.e. sets of smaller
image patches). However, such processing is typically performed at a single
scale (e.g., 20x magnification) of WSIs, disregarding the vital inter-scale
information that is key to diagnoses by human pathologists. In this study, we
propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale
relationships into a single MIL network for pathological image diagnosis. The
contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL)
algorithm that integrates the multi-scale information and the inter-scale
relationships is proposed; (2) A toy dataset with scale-specific morphological
features is created and released to examine and visualize differential
cross-scale attention; (3) Superior performance on both in-house and public
datasets is demonstrated by our simple cross-scale MIL strategy. The official
implementation is publicly available at this https URL

研究了一种新的跨尺度多实例学习算法，并将其应用于病理学影像诊断中，通过整合多尺度信息和跨尺度关系来提高诊断准确率。在内部和公共数据集上均表现出优异的性能。