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Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Open accessAbstract
The development of decision support systems for pathology and their deployment in clinical practice have been hindered by the need for large manually annotated datasets. To overcome this problem, we present a multiple instance learning-based deep learning system that uses only the reported diagnoses as labels for training, thereby avoiding expensive and time-consuming pixel-wise manual annotations. We evaluated this framework at scale on a dataset of 44,732 whole slide images from 15,187 patients without any form of data curation. Tests on prostate cancer, basal cell carcinoma and breast cancer metastases to axillary lymph nodes resulted in areas under the curve above 0.98 for all cancer types. Its clinical application would allow pathologists to exclude 65-75% of slides while retaining 100% sensitivity. Our results show that this system has the ability to train accurate classification models at unprecedented scale, laying the foundation for the deployment of computational decision support systems in clinical practice.
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“Prognostic and risk stratification systems estimate outcomes such as mortality, deterioration, readmission, or complications, supporting early intervention and clinical prioritisation [15,33].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [33]
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“Large-scale experiments have shown that, although dependent on the tissue type and specific task, at least 10,000 WSIs are necessary for clinical-grade classification performance (Campanella et al, 2019), which is very difficult to achieve for rare diseases, such as MF.”
From Deep Learning–Based Classification of Early-Stage Mycosis Fungoides and Benign Inflammatory Dermatoses on H&E-Stained Whole-Slide Images: A Retrospective, Proof-of-Concept Study (2025) · cites this paper as Campanella et al, 2019
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“However, most importantly, the performance of DL-based models significantly improves with increasing numbers of patients included in the training dataset.”
From Deep Learning–Based Classification of Early-Stage Mycosis Fungoides and Benign Inflammatory Dermatoses on H&E-Stained Whole-Slide Images: A Retrospective, Proof-of-Concept Study (2025) · cites this paper as Campanella et al, 2019
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“AI and machine learning techniques are indispensable for handling the high-dimensional data generated by digital pathology, multi-omics analyses, and clinical information [23].”
From A perspective on integrating digital pathology, proteomics, clinical data and AI analytics in cancer research (2025) · cites this paper as [23]
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“Digital pathology systems analyse whole-slide images to detect tumours and grade malignancies with reported high sensitivity in selected study settings [33,16].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [33]
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“Paige.ai has received breakthrough status from FDA for an AI-based algorithm that is capable of diagnose cancer in computational histopathology with great accuracy, allowing pathologist to gain time to focus on important slides (44).”
From Artificial Intelligence in Medicine: Today and Tomorrow (2020) · cites this paper as [44]
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“However, training robust AI systems requires large datasets for which data collection faces practical, ethical and legal obstacles.”
From Swarm learning for decentralized artificial intelligence in cancer histopathology (2022) · cites this paper as [19]
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“Advances in artificial intelligence (AI) are driving a revolution across science, enabling exciting new results across chemistry,80–82 biology,83,84 and medicine.85–88”
From Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review (2021) · cites this paper as [86]
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“Forecasting capabilities enable planning for seasonal demand, public health emergencies, and fluctuations in elective procedures [33].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [33]
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“AI is also used to automate radiotherapy contouring and optimise dose distribution, improving consistency in treatment delivery [33,36].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [33]
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“Previous work has shown that when the sample size is increased to approximately 10,000 patients, classifier performance will increase 19,20 .”
From Swarm learning for decentralized artificial intelligence in cancer histopathology (2022) · cites this paper as [19]
In the citing paper
“Using multiple-instance learning14,49,55 with labels at the level of groups of slides has enabled clinically relevant diagnostics by scaling to training datasets on the order of 10,000 WSIs10–14.”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [14]
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“Campanella et al. (2019) train a weakly supervised segmentation model for whole slide histopathology images using only slide-level labels.”
From Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation (2020) · cites this paper as Campanella et al. (2019)
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“They achieve a 1% increase in AUC when using the RNN classifier over just using the MIL approach directly.”
From Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation (2020) · cites this paper as Campanella et al. (2019)
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Metadata
- Authors
- 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.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- Corresponding author
- Fuchs, Thomas J.
- ORCID
- Venue
- Nature Medicine
- Published
- 2019-07-15
- Volume
- 25
- Issue
- 8
- Pages
- 1301-1309
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC7418463
- OpenAlex
- W2956228567
- Cited by
- 2,799
- References
- 44
- Citations by year
- 2026: 585 · 2025: 457 · 2024: 435 · 2023: 400 · 2022: 366 · 2021: 304 · 2020: 216 · 2019: 29
- FWCI
- 104.81
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (P30CA008748)
- 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
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- computational pathology, weakly supervised learning, multiple instance learning, whole slide images, cancer diagnosis, prostate cancer, basal cell carcinoma, breast cancer metastasis, axillary lymph nodes, decision support system, annotation-free training, clinical implementation
- MeSH terms
- Deep Learning, Breast Neoplasms, Carcinoma, Basal Cell, Basal Cell Carcinoma, Female, Humans, Male, Prostatic Neoplasms, Decision Support Systems, Clinical, Neoplasm Grading
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