Cross-scale multi-instance learning for pathological image diagnosis

Ruining Deng

doi:10.1016/j.media.2024.103124

Cross-scale multi-instance learning for pathological image diagnosis

2024-05-01

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DOI：10.1016/j.media.2024.103124

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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. , 20 x magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel crossscale 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 crossscale 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 https://github.com/hrlblab/CSMIL.

Keywords:

Multi-instance learning

Multi-scale

Attention mechanism

Pathology

Cross-scale multi-instance learning for pathological image diagnosis

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