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Cited by 116AI in cancer detectionDomain Adaptation and Few-Shot LearningRadiomics and Machine Learning in Medical Imaging

The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.

Self-supervised driven consistency training for annotation efficient histopathology image analysis

Open access
Srinidhi, Chetan L., Kim, Seung Wook, Chen, Fu-Der, Martel, Anne L.·2022·Medical Image Analysis
DOI 10.1016/j.media.2021.102256
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Abstract

Training a neural network with a large labeled dataset is still a dominant paradigm in computational histopathology. However, obtaining such exhaustive manual annotations is often expensive, laborious, and prone to inter and intra-observer variability. While recent self-supervised and semi-supervised methods can alleviate this need by learning unsupervised feature representations, they still struggle to generalize well to downstream tasks when the number of labeled instances is small. In this work, we overcome this challenge by leveraging both task-agnostic and task-specific unlabeled data based on two novel strategies: (i) a self-supervised pretext task that harnesses the underlying multi-resolution contextual cues in histology whole-slide images to learn a powerful supervisory signal for unsupervised representation learning; (ii) a new teacher-student semi-supervised consistency paradigm that learns to effectively transfer the pretrained representations to downstream tasks based on prediction consistency with the task-specific unlabeled data. We carry out extensive validation experiments on three histopathology benchmark datasets across two classification and one regression based tasks, i.e., tumor metastasis detection, tissue type classification, and tumor cellularity quantification. Under limited-label data, the proposed method yields tangible improvements, which is close to or even outperforming other state-of-the-art self-supervised and supervised baselines. Furthermore, we empirically show that the idea of bootstrapping the self-supervised pretrained features is an effective way to improve the task-specific semi-supervised learning on standard benchmarks. Code and pretrained models are made available at: https://github.com/srinidhiPY/SSL_CR_Histo.

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Research works printed but not linked: 14

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Metadata

Authors
Srinidhi, C. L., Kim, S. W., Chen, F.-D., & Martel, A. L.
Affiliations
University of Toronto (CA)
Corresponding author
Srinidhi, Chetan L.
ORCID
Martel, Anne L.
Venue
Medical Image Analysis
Publisher
Elsevier BV
Published
2022-01
Volume
75
Pages
102256
ISSN
1361-8415 (ISSN-L)
DOI
10.1016/j.media.2021.102256
OpenAlex
W3128158496
Cited by
116
References
51
Citations by year
2026: 10 · 2025: 17 · 2024: 35 · 2023: 31 · 2022: 18
FWCI
7.86
Type
journal article
Language
en
Open access
Licence
Locations
  • Medical Image Analysis (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Canadian Institutes of Health Research
  • Canadian Cancer Society (705772)
  • Compute Canada
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
self-supervised learning, consistency regularization, teacher-student learning, semi-supervised learning, histopathology image analysis, whole slide images, limited labeled data, unsupervised feature learning, tumor metastasis detection, tissue classification, annotation efficiency
MeSH terms
Supervised Machine Learning, Humans, Image Processing, Computer-Assisted, Neural Networks, Computer, Benchmarking

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