Cited by 960AI in cancer detectionRadiomics and Machine Learning in Medical ImagingDomain Adaptation and Few-Shot Learning

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Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning

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Li, Bin, Li, Yin, Eliceiri, Kevin W.·2021·2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

DOI 10.1109/cvpr46437.2021.01409PMC PMC8765709

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Abstract

We address the challenging problem of whole slide image (WSI) classification. WSIs have very high resolutions and usually lack localized annotations. WSI classification can be cast as a multiple instance learning (MIL) problem when only slide-level labels are available. We propose a MIL-based method for WSI classification and tumor detection that does not require localized annotations. Our method has three major components. First, we introduce a novel MIL aggregator that models the relations of the instances in a dual-stream architecture with trainable distance measurement. Second, since WSIs can produce large or unbalanced bags that hinder the training of MIL models, we propose to use self-supervised contrastive learning to extract good representations for MIL and alleviate the issue of prohibitive memory cost for large bags. Third, we adopt a pyramidal fusion mechanism for multiscale WSI features, and further improve the accuracy of classification and localization. Our model is evaluated on two representative WSI datasets. The classification accuracy of our model compares favorably to fully-supervised methods, with less than 2% accuracy gap across datasets. Our results also outperform all previous MIL-based methods. Additional benchmark results on standard MIL datasets further demonstrate the superior performance of our MIL aggregator on general MIL problems.

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Authors: Li, B., Li, Y., & Eliceiri, K. W.
Affiliations: University of Wisconsin–Madison (US)
Corresponding author: Li, Yin
Eliceiri, Kevin W.
ORCID: Li, Bin
Li, Yin
Eliceiri, Kevin W.
Venue: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher: IEEE
Published: 2021-06
Volume: 2021
Pages: 14313-14323
ISSN: 1063-6919 (ISSN-L)
DOI: 10.1109/cvpr46437.2021.01409
PMC: PMC8765709
OpenAlex: W3176719058
Cited by: 957
References: 60
Citations by year: 2026: 204 · 2025: 247 · 2024: 241 · 2023: 171 · 2022: 82 · 2021: 12
FWCI: 58.33
Type: conference paper
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: Semiconductor Research Corporation
Wisconsin Alumni Research Foundation
National Institutes of Health
National Institute of General Medical Sciences (P41GM135019)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords: whole slide image classification, multiple instance learning, self-supervised contrastive learning, two-stream network, tumor detection, multi-scale features

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Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning

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