CS-CO: A Hybrid Self-Supervised Visual Representation Learning Method for H&E-stained Histopathological Images
Abstract
Visual representation extraction is a fundamental problem in the field of computational histopathology. Considering the powerful representation capacity of deep learning and the scarcity of annotations, self-supervised learning has emerged as a promising approach to extract effective visual representations from unlabeled histopathological images. Although a few self-supervised learning methods have been specifically proposed for histopathological images, most of them suffer from certain defects that may hurt the versatility or representation capacity. In this work, we propose CS-CO, a hybrid self-supervised visual representation learning method tailored for H&E-stained histopathological images, which integrates advantages of both generative and discriminative approaches. The proposed method consists of two self-supervised learning stages: cross-stain prediction (CS) and contrastive learning (CO). In addition, a novel data augmentation approach named stain vector perturbation is specifically proposed to facilitate contrastive learning. Our CS-CO makes good use of domain-specific knowledge and requires no side information, which means good rationality and versatility. We evaluate and analyze the proposed CS-CO on three H&E-stained histopathological image datasets with downstream tasks of patch-level tissue classification and slide-level cancer prognosis and subtyping. Experimental results demonstrate the effectiveness and robustness of the proposed CS-CO on common computational histopathology tasks. Furthermore, we also conduct ablation studies and prove that cross-staining prediction and contrastive learning in our CS-CO can complement and enhance each other. Our code is made available at https://github.com/easonyang1996/CS-CO.
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Transpath and CS-CO for histopathological image analysis reduced costs for histopathological image annotations.
“Typical examples include Transpath [148] and CS-CO [149] for histopathological image analysis, which reduced the costs for histopathological image annotations.”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [149]
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- Authors
- Yang, P., Yin, X., Lu, H., Hu, Z., Zhang, X., Jiang, R., & Lv, H.
- Affiliations
- Beijing National Research Center for Information Science and Technology (CN)
- Central South University (CN)
- Corresponding author
- Lv, Hairong
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2022-10
- Volume
- 81
- Pages
- 102539
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W4286251888
- Cited by
- 50
- References
- 76
- Citations by year
- 2026: 5 · 2025: 19 · 2024: 14 · 2023: 11 · 2022: 1
- FWCI
- 3.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- 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
- self-supervised learning, visual representation learning, contrastive learning, data augmentation, tissue classification, cancer prognosis, cancer subtyping, computational histopathology
- MeSH terms
- Supervised Machine Learning, Histological Techniques, Staining and Labeling
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