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Cross-scale multi-instance learning for pathological image diagnosis
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DOI：10.1016/j.media.2024.103124.png)
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Abstract
En 中文
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
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11.8
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3.9K
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