Cross-scale multi-instance learning for pathological image diagnosis
Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved..
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× 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 https://github.com/hrlblab/CS-MIL.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:94 |
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| Enthalten in: |
Medical image analysis - 94(2024) vom: 01. Apr., Seite 103124 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Deng, Ruining [VerfasserIn] |
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| Links: |
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| Themen: |
Attention mechanism |
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| Anmerkungen: |
Date Completed 16.04.2024 Date Revised 25.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.media.2024.103124 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM369181557 |
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| 520 | |a 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× 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 https://github.com/hrlblab/CS-MIL | ||
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| 650 | 4 | |a Multi-scale | |
| 650 | 4 | |a Pathology | |
| 700 | 1 | |a Cui, Can |e verfasserin |4 aut | |
| 700 | 1 | |a Remedios, Lucas W |e verfasserin |4 aut | |
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| 700 | 1 | |a Womick, R Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Chiron, Sophie |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jia |e verfasserin |4 aut | |
| 700 | 1 | |a Roland, Joseph T |e verfasserin |4 aut | |
| 700 | 1 | |a Lau, Ken S |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Qi |e verfasserin |4 aut | |
| 700 | 1 | |a Wilson, Keith T |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yaohong |e verfasserin |4 aut | |
| 700 | 1 | |a Coburn, Lori A |e verfasserin |4 aut | |
| 700 | 1 | |a Landman, Bennett A |e verfasserin |4 aut | |
| 700 | 1 | |a Huo, Yuankai |e verfasserin |4 aut | |
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