Self supervised contrastive learning for digital histopathology
Open accessAbstract
Unsupervised learning has been a long-standing goal of machine learning and is especially important for medical image analysis, where the learning can compensate for the scarcity of labeled datasets. A promising subclass of unsupervised learning is self-supervised learning, which aims to learn salient features using the raw input as the learning signal. In this work, we tackle the issue of learning domain-specific features without any supervision to improve multiple task performances that are of interest to the digital histopathology community. We apply a contrastive self-supervised learning method to digital histopathology by collecting and pretraining on 57 histopathology datasets without any labels. We find that combining multiple multi-organ datasets with different types of staining and resolution properties improves the quality of the learned features. Furthermore, we find using more images for pretraining leads to a better performance in multiple downstream tasks, albeit there are diminishing returns as more unlabeled images are incorporated into the pretraining. Linear classifiers trained on top of the learned features show that networks pretrained on digital histopathology datasets perform better than ImageNet pretrained networks, boosting task performances by more than 28% in F1 scores on average. Interestingly, we did not observe a consistent correlation between the pretraining dataset site or the organ versus the downstream task (e.g., pretraining with only breast images does not necessarily lead to a superior downstream task performance for breast-related tasks). These findings may also be useful when applying newer contrastive techniques to histopathology data. Pretrained PyTorch models are made publicly available at https://github.com/ozanciga/self-supervised-histopathology.
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Based on contrastive learning and data augmentation, self-supervised learning can learn robust data embeddings from unlabeled data and the trained model can be fitted to many downstream tasks.
“Based on contrastive learning and data augmentation, the self-supervised learning approach can learn robust data embeddings from unlabeled data and the trained model can be fitted to many downstream tasks [146,147].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [147]
Self-supervised learning is an important approach for addressing data scarcity by fully leveraging unlabeled data.
“Self-supervised learning is an important approach for addressing the challenge by fully leveraging the unlabeled data.”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [147]
The pathology tasks were focused on histopathological classification, with the majority focused on colorectal cancer classification.
“The tasks were focused on histopathological classification, where the majority focused on colorectal cancer classification68,84–87.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [85]
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- Authors
- Ciga, O., Xu, T., & Martel, A. L.
- Affiliations
- University of Toronto (CA)
- University of British Columbia (CA)
- Corresponding author
- Ciga, Ozan
- Venue
- Machine Learning with Applications
- Publisher
- Elsevier BV
- Published
- 2022-03
- Volume
- 7
- Pages
- 100198
- ISSN
- 2666-8270 (ISSN-L)
- OpenAlex
- W3108181344
- Cited by
- 294
- References
- 55
- Citations by year
- 2026: 31 · 2025: 58 · 2024: 85 · 2023: 72 · 2022: 38 · 2021: 10
- FWCI
- 18.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Machine Learning with Applications (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- Compute Canada
- Natural Sciences and Engineering Research Council of Canada
- Canadian Cancer Society (705772)
- 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, contrastive learning, digital histopathology, unsupervised feature learning, transfer learning, breast histopathology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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