Data-efficient and weakly supervised computational pathology on whole-slide images
Open accessAbstract
Deep-learning methods for computational pathology require either manual annotation of gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and typically suffer from poor domain adaptation and interpretability. Here we report an interpretable weakly supervised deep-learning method for data-efficient WSI processing and learning that only requires slide-level labels. The method, which we named clustering-constrained-attention multiple-instance learning (CLAM), uses attention-based learning to identify subregions of high diagnostic value to accurately classify whole slides and instance-level clustering over the identified representative regions to constrain and refine the feature space. By applying CLAM to the subtyping of renal cell carcinoma and non-small-cell lung cancer as well as the detection of lymph node metastasis, we show that it can be used to localize well-known morphological features on WSIs without the need for spatial labels, that it overperforms standard weakly supervised classification algorithms and that it is adaptable to independent test cohorts, smartphone microscopy and varying tissue content.
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Whole-slide representation models can be pretrained to distill pathology-specific knowledge from large WSI collections, simplifying clinical endpoint prediction with their off-the-shelf application.
“Instead of training an additional model on top of patch embeddings from scratch34,73–80, these whole-slide representation models can be pretrained to distill pathology-specific knowledge from large WSI collections, simplifying clinical endpoint prediction with their off-the-shelf application.”
From A multimodal whole-slide foundation model for pathology (2025) · cites this paper as [74]
Predicted tumor cell density matched that of CLAM, further validating Loki Decompose's robustness.
“Additionally, our predicted tumor cell density matched that of CLAM40, further validating Loki Decompose’s robustness (Fig. 5g).”
From A visual–omics foundation model to bridge histopathology with spatial transcriptomics (2025) · cites this paper as [40]
Higher similarity scores were consistent with higher diagnostic values of tumors calculated by CLAM.
“Additionally, higher similarity scores were consistent with higher diagnostic values of tumors calculated by clustering-constrained-attention multiple-instance learning40 (CLAM, a SOTA WSI tumor analysis model; Fig. 3b).”
From A visual–omics foundation model to bridge histopathology with spatial transcriptomics (2025) · cites this paper as [40]
Transfer learning allows developers to adapt models originally trained on general photographs to detect lung nodules or classify pathology slides using only thousands of examples.
“Rather than requiring millions of oncology-specific images, developers can adapt models originally trained on general photographs to detect lung nodules or classify pathology slides using only thousands of examples [127,128,129].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [128]
Lu and colleagues used a weakly supervised deep learning method called CLAM to predict subtypes in renal cell carcinoma and NSCLC.
“Lu and colleagues (69) used a weakly supervised deep learning method-dubbed clustering-constrained-attention multiple-instance learning (CLAM)-to predict subtypes in renal cell carcinoma and NSCLC.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [69]
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- Authors
- Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Chen, R. J., Barbieri, M., & Mahmood, F.
- Affiliations
- Broad Institute (US)
- Brigham and Women's Hospital (US)
- Corresponding author
- Mahmood, Faisal
- ORCID
- Venue
- Nature Biomedical Engineering
- Published
- 2021-03-01
- Volume
- 5
- Issue
- 6
- Pages
- 555-570
- ISSN
- 2157-846X (ISSN-L)
- PMC
- PMC8711640
- OpenAlex
- W3018295606
- Cited by
- 2,282
- References
- 48
- Citations by year
- 2026: 682 · 2025: 603 · 2024: 452 · 2023: 307 · 2022: 169 · 2021: 68 · 2020: 1
- FWCI
- 87.99
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R35GM138216A)
- National Science Foundation
- U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (T32HG002295)
- Google Cloud Research Award, Nvidia GPU Grant Program, BWH Pathology Internal Funds
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- whole slide images, weakly supervised learning, data-efficient deep learning, multiple instance learning, interpretability, domain adaptation, non-small cell lung cancer, lymph node metastasis detection, smartphone microscopy
- MeSH terms
- Smartphone, Deep Learning, Carcinoma, Non-Small-Cell Lung, Carcinoma, Renal Cell, Histocytochemistry, Humans, Image Interpretation, Computer-Assisted, Kidney Neoplasms, Lung Neoplasms, Lymphatic Metastasis, Microscopy, Area Under Curve
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