Improving Representation Learning for Histopathologic Images with Cluster Constraints
Open accessAbstract
Recent advances in whole-slide image (WSI) scanners and computational capabilities have significantly propelled the application of artificial intelligence in histopathology slide analysis. While these strides are promising, current supervised learning approaches for WSI analysis come with the challenge of exhaustively labeling high-resolution slides-a process that is both labor-intensive and timeconsuming. In contrast, self-supervised learning (SSL) pretraining strategies are emerging as a viable alternative, given that they don't rely on explicit data annotations. These SSL strategies are quickly bridging the performance disparity with their supervised counterparts. In this context, we introduce an SSL framework. This framework aims for transferable representation learning and semantically meaningful clustering by synergizing invariance loss and clustering loss in WSI analysis. Notably, our approach outperforms common SSL methods in downstream classification and clustering tasks, as evidenced by tests on the Camelyon16 and a pancreatic cancer dataset. The code and additional details are accessible at https://github.com/wwyi1828/CluSiam.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Foundation models can learn from vast amounts of unlabelled data, considerably reducing the need for manual annotation.
“Another advantage lies in its ability to learn from vast amounts of unlabelled data, thereby considerably reducing the need for manual annotation17.”
From Benchmarking foundation models as feature extractors for weakly supervised computational pathology (2025) · cites this paper as [17]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Momentum Contrast for Unsupervised Visual Representation Learning
He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00975 · Cited by 13,347 · see citing works
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020) · arXiv · doi:10.48550/arxiv.2002.05709 · Cited by 10,857 · see citing works
Representation Learning with Contrastive Predictive Coding
Oord, A. V. D., Li, Y., & Vinyals, O. (2018) · arXiv · doi:10.48550/arxiv.1807.03748 · Cited by 6,242 · cited 2× · see citing works
“One of the key paradigms of SSL is contrastive-based SSL [19, 11, 35, 38, 25].”
BackgroundMethodEmerging Properties in Self-Supervised Vision Transformers
Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00951 · Cited by 5,638 · cited 1× · see citing works
“To avoid the need for negative pairs, some knowledge-distillation-based methods [12, 6, 18] concentrate solely on positive sample pairs, which are defined us-ing augmented views of the same image.”
BackgroundBootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., et al. (2020) · arXiv · doi:10.48550/arxiv.2006.07733 · Cited by 3,846 · see citing works
Exploring Simple Siamese Representation Learning
Chen, X., & He, K. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01549 · Cited by 3,725 · see citing works
Categorical Reparameterization with Gumbel-Softmax
Jang, E., Gu, S., & Poole, B. (2016) · arXiv · doi:10.48550/arxiv.1611.01144 · Cited by 3,616 · see citing works
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., & Fuchs, T. J. (2019) · Nature Medicine · doi:10.1038/s41591-019-0508-1 · Cited by 2,799 · see citing works
Deep Clustering for Unsupervised Learning of Visual Features
Caron, M., Bojanowski, P., Joulin, A., & Douze, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01264-9_9 · Cited by 2,674 · see citing works
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., & Joulin, A. (2020) · arXiv · doi:10.48550/arxiv.2006.09882 · Cited by 2,499 · see citing works
Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases
Janowczyk, A. R., & Madabhushi, A. (2016) · Journal of Pathology Informatics · doi:10.4103/2153-3539.186902 · Cited by 1,355 · see citing works
Barlow Twins: Self-Supervised Learning via Redundancy ReductionPreprint
Žbontar, J., Jing, L., Misra, I., LeCun, Y., & Deny, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.03230 · Cited by 1,329 · see citing works
Attention-based Deep Multiple Instance LearningPreprint
Ilse, M., Tomczak, J. M., & Welling, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.04712 · Cited by 1,235 · see citing works
Deep Learning for Identifying Metastatic Breast CancerPreprint
Wang, D., Khosla, A., Gargeya, R., Irshad, H., & Beck, A. H. (2016) · arXiv.org · doi:10.48550/arxiv.1606.05718 · Cited by 1,028 · see citing works
Li, B., Li, Y., & Eliceiri, K. W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01409 · Cited by 957 · see citing works
Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification
Hou, L., Samaras, D., Kurç, T., Gao, Y., Davis, J., & Saltz, J. H. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.266 · Cited by 902 · see citing works
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning
Chen, R. J., Chen, C., Li, Y., Chen, T. Y., Trister, A. D., Krishnan, R. G., & Mahmood, F. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01567 · Cited by 621 · see citing works
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised LearningPreprint
Bardes, A., Ponce, J., & LeCun, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.04906 · Cited by 617 · see citing works
Prototypical Contrastive Learning of Unsupervised Representations
Li, J., Zhou, P., Xiong, C., & Hoi, S. C. H. (2020) · arXiv · doi:10.48550/arxiv.2005.04966 · Cited by 611 · see citing works
SCAN: Learning to Classify Images Without Labels
Gansbeke, W. V., Vandenhende, S., Georgoulis, S., Proesmans, M., & Gool, L. V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58607-2_16 · Cited by 561 · see citing works
Detecting Cancer Metastases on Gigapixel Pathology Images
Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G. E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P. Q., Corrado, G. S., et al. (2017) · arXiv · doi:10.48550/arxiv.1703.02442 · Cited by 506 · see citing works
1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset
Litjens, G. J. S., Bándi, P., Bejnordi, B. E., Geessink, O. G. F., Balkenhol, M. C. A., Bult, P., Halilović, A., Hermsen, M., van de Loo, R., Vogels, R. J., et al. (2018) · GigaScience · doi:10.1093/gigascience/giy065 · Cited by 414 · see citing works
With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations
Dwibedi, D., Aytar, Y., Tompson, J., Sermanet, P., & Zisserman, A. (2021) · arXiv · doi:10.48550/arxiv.2104.14548 · Cited by 405 · see citing works
With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations
Dwibedi, D., Aytar, Y., Tompson, J., Sermanet, P., & Zisserman, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00945 · Cited by 404 · see citing works
Wei, J., Tafe, L. J., Linnik, Y., Vaickus, L. J., Tomita, N., & Hassanpour, S. (2019) · Scientific Reports · doi:10.1038/s41598-019-40041-7 · Cited by 314 · see citing works
Self-citationChen, C., Chen, C., Yu, W.-H., Chen, S. S., Chang, Y., Hsu, T., Hsiao, M., Yeh, C., & Chen, C. (2021) · Nature Communications · doi:10.1038/s41467-021-21467-y · Cited by 221 · see citing works
RetCCL: Clustering-guided contrastive learning for whole-slide image retrieval
Wang, X., Du, Y., Yang, S., Zhang, J., Wang, M., Zhang, J., Yang, W., Huang, J., & Han, X. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102645 · Cited by 218 · cited 1× · see citing works
“[41] employed a K-Means clustering-driven architecture, while Chen et al .”
MethodZhao, Y. S., Yang, F., Fang, Y., Liu, H., Zhou, N., Zhang, J., Sun, J., Yang, S., Menze, B. H., Fan, X., et al. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00489 · Cited by 206 · see citing works
Tomita, N., Abdollahi, B., Wei, J., Ren, B., Suriawinata, A., & Hassanpour, S. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.14645 · Cited by 204 · see citing works
Self-citationBenchmarking Self-Supervised Learning on Diverse Pathology Datasets
Kang, M., Song, H., Park, S., Yoo, D., & Pereira, S. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00326 · Cited by 187 · see citing works
Mercan, C., Aksoy, S., Mercan, E., Shapiro, L. G., Weaver, D. L., & Elmore, J. G. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2758580 · Cited by 132 · see citing works
Fast and scalable search of whole-slide images via self-supervised deep learning
Chen, C., Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Schaumberg, A. J., & Mahmood, F. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00929-8 · Cited by 130 · see citing works
Weakly Supervised Multiple Instance Learning Histopathological Tumor Segmentation
Lerousseau, M., Vakalopoulou, M., Classe, M., Adam, J., Battistella, E., Carré, A., Estienne, T., Henry, T., Deutsch, É., & Nikos Paragios, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59722-1_45 · Cited by 104 · see citing works
Wahab, N., Miligy, I. M., Dodd, K., Sahota, H., Toss, M. S., Lu, W., Jahanifar, M., Bilal, M., Graham, S., Park, Y., et al. (2022) · The Journal of Pathology Clinical Research · doi:10.1002/cjp2.256 · Cited by 52 · see citing works
Pancreatic Cancer Detection in Whole Slide Images Using Noisy Label Annotations
Le, H., Samaras, D., Kurç, T., Gupta, R., Shroyer, K. R., & Saltz, J. H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32239-7_60 · Cited by 47 · see citing works
Tomita, N., Tafe, L. J., Suriawinata, A. A., Tsongalis, G. J., Nasir-Moin, M., Dragnev, K. H., & Hassanpour, S. (2022) · Translational Oncology · doi:10.1016/j.tranon.2022.101494 · Cited by 41 · see citing works
Self-citationResolution-based distillation for efficient histology image classification
DiPalma, J., Suriawinata, A. A., Tafe, L. J., Torresani, L., & Hassanpour, S. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102136 · Cited by 38 · see citing works
Self-citationSelf-similarity Student for Partial Label Histopathology Image Segmentation
Cheng, H.-T., Yeh, C.-F., Kuo, P.-C., Wei, A., Liu, K.-C., Ko, M.-C., Chao, K.-H., Peng, Y., & Liu, T.-L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58595-2_8 · Cited by 25 · see citing works
A Comparison Between Single- and Multi-Scale Approaches for Classification of Histopathology Images
D'Amato, M., Szostak, P., & Torben-Nielsen, B. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.892658 · Cited by 25 · see citing works
Ashraf, M., Robles, W. R. Q., Kim, M., Ko, Y. S., & Yi, M. Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-05001-8 · Cited by 24 · see citing works
Masked pre-training of transformers for histology image analysis
Jiang, S., Hondelink, L. M., Suriawinata, A. A., & Hassanpour, S. (2024) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2024.100386 · Cited by 23 · see citing works
Self-citationConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Images
Yang, J., Chen, H., Liang, Y., Huang, J., He, L., & Yao, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19803-8_31 · Cited by 21 · see citing works
Graph Convolutional Neural Networks for Histologic Classification of Pancreatic Cancer
Wu, W., Liu, X., Hamilton, R. B., Suriawinata, A. A., & Hassanpour, S. (2023) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2022-0035-oa · Cited by 14 · see citing works
Self-citationNon-Linguistic Supervision for Contrastive Learning of Sentence Embeddings
Jian, Y., Gao, C., & Vosoughi, S. (2022) · neural information processing systems · doi:10.52202/068431-2575 · Cited by 9 · see citing works
Self-citationDiPalma, J., Torresani, L., & Hassanpour, S. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100320 · Cited by 7 · see citing works
Self-citationNon-Linguistic Supervision for Contrastive Learning of Sentence EmbeddingsPreprint
Jian, Y., Gao, C., & Vosoughi, S. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2209.09433 · Cited by 6 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Carse Jacob, Carey Frank, and McKenna Stephen. Unsupervised representation learning from pathology images with multi-directional contrastive predictive coding. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pages 1254-1258. IEEE, 2021.
Cited by
Sorted by citations
Benchmarking foundation models as feature extractors for weakly supervised computational pathology
Neidlinger, P., El Nahhas, O. S. M., Muti, H. S., Lenz, T., Hoffmeister, M., Brenner, H., van Treeck, M., Langer, R., Dislich, B., Behrens, H. M., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01516-3 · Cited by 80 · see citing works
Unsupervised Generative Fake Image Detector
Qiao, T., Shao, H., Xie, S., & Shi, R. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3383833 · Cited by 15 · see citing works
Shao, W., Shi, Y., Zhang, D., Zhou, J., & Wan, P. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01111 · Cited by 16 · see citing works
Mammadov, A., Folgoc, L. L., Adam, J., Buronfosse, A., Hayem, G., Hocquet, G., & Gori, P. (2025) · Journal of medical imaging · doi:10.1117/1.jmi.12.6.061404 · Cited by 6 · see citing works
GMMamba: Group Masking Mamba for Whole Slide Image Classification
Zheng, T., Yao, H., Jiang, K., Xiao, Y., & Zhao, S. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00926 · Cited by 5 · see citing works
Li, C., Huang, P., Qin, J., & Luo, X. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3609838 · Cited by 4 · see citing works
Gupta, E., & Gupta, V. (2024) · Health Information Science and Systems · doi:10.1007/s13755-024-00316-4 · Cited by 3 · see citing works
Yang, C., Liu, L., Xiao, W., Wu, F., Guo, L., & Li, C. (2026) · Scientific Reports · doi:10.1038/s41598-026-44633-y · Cited by 1 · see citing works
Exploiting Label-Independent Regularization from Spatial Patterns for Whole Slide Image Analysis
Wu, W., Xu, X., Gao, C., Diao, X., Li, S. X., & Gui, J. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00833 · Cited by 1 · see citing works
Self-citationLi, J., Kuang, H., Liu, J., Yue, H., & Wang, J. (2026) · Big Data Mining and Analytics · doi:10.26599/bdma.2025.9020072 · Cited by 1 · see citing works
Shi, Y., Zhu, Q., Zuo, Y., Wan, P., Zhang, D., & Shao, W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73290-4_21 · Cited by 1 · see citing works
MH-CLR: A Self-Supervised Learning Strategy Based on Hard Sample Mining and Masked Autoencoders
Yang, C., Li, C., & Guo, L. (2024) · IEEE International Conference on Anti-counterfeiting, Security, and Identification (ASID) · doi:10.1109/asid63618.2024.10839687 · Cited by 1 · see citing works
Notton, C., Sharma, V., Trinh, V. Q., Chen, L., Xu, M., Varma, S., & Hosseini, M. S. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104004 · Cited by 1 · see citing works
Huang, M., Xiao, W., Lin, G., Li, C., Xu, H., Huang, Y., Chen, S., Chen, C., Sun, Y., & Zhong, Q. (2025) · BMC Cancer · doi:10.1186/s12885-025-15459-0
Pathology-Aligned Contrastive Representation Learning for Gleason Grading
Huang, M., Zhong, J., Wu, J., He, J., Li, Z., Shi, P., & Zhu, X. (2026) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2026.3685011
Learning Spatial-Preserving Hierarchical Representations for Digital Pathology
Wu, W., Diao, X., Zhang, C., Gao, C., Xu, X., Li, S., & Gui, J. (2026) · doi:10.1109/cvprf71909.2026.00527
Self-citation
- Authors
- Wu, W., Gao, C., DiPalma, J., Vosoughi, S., & Hassanpour, S.
- Affiliations
- Dartmouth College (US)
- Northwestern University (US)
- Venue
- 2023 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2023-10-01
- Volume
- 2023
- Pages
- 21347-21357
- PMC
- PMC11062482
- OpenAlex
- W4390874474
- Cited by
- 16
- References
- 46
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 5
- FWCI
- 2.83
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Cancer Institute
- 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 images, histopathology, self-supervised learning, representation learning, clustering loss, transfer learning, Camelyon16, pancreatic cancer, image classification
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 ›
Are you an author, or do you see a problem with this page? Request a review
