Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach
Open accessAbstract
Breast cancer remains a global challenge, causing over 600,000 deaths in 2018 (ref. 1). To achieve earlier cancer detection, health organizations worldwide recommend screening mammography, which is estimated to decrease breast cancer mortality by 20-40% (refs. 2,3). Despite the clear value of screening mammography, significant false positive and false negative rates along with non-uniformities in expert reader availability leave opportunities for improving quality and access 4,5. To address these limitations, there has been much recent interest in applying deep learning to mammography 6-18, and these efforts have highlighted two key difficulties: obtaining large amounts of annotated training data and ensuring generalization across populations, acquisition equipment and modalities. Here we present an annotation-efficient deep learning approach that (1) achieves state-of-the-art performance in mammogram classification, (2) successfully extends to digital breast tomosynthesis (DBT; '3D mammography'), (3) detects cancers in clinically negative prior mammograms of patients with cancer, (4) generalizes well to a population with low screening rates and (5) outperforms five out of five full-time breast-imaging specialists with an average increase in sensitivity of 14%. By creating new 'maximum suspicion projection' (MSP) images from DBT data, our progressively trained, multiple-instance learning approach effectively trains on DBT exams using only breast-level labels while maintaining localization-based interpretability. Altogether, our results demonstrate promise towards software that can improve the accuracy of and access to screening mammography worldwide.
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A subsequent study reported an AI approach covering 2D and 3D mammography that detected breast cancer 1 to 2 years earlier than standard radiographic review in some cases.
“A subsequent study presented an AI approach that encompasses both 2D and 3D mammography, with evidence of detecting breast cancer 1 to 2 years earlier than standard radiographic review for some cases (10).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [10]
There are efforts to generate more diverse public datasets, such as EMBED, and instances where AI performance has generalized well across different patient populations.
“Conversely, there are efforts to generate more diverse public data sets, such as EMBED (180), and instances where AI performance has generalized well across different patient populations (9,10,12).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [10]
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- Authors
- Lotter, W., Diab, A. R., Haslam, B., Kim, J. G., Grisot, G., Wu, E., Wu, K., Onieva, J. O., Boyer, Y., Boxerman, J. L., Wang, M., Bandler, M., Vijayaraghavan, G. R., & Gregory Sorensen, A.
- Affiliations
- Stanford University (US)
- Brown University (US)
- Henan Provincial People's Hospital (CN)
- Medford Radiology Group (US)
- University of Massachusetts Chan Medical School (US)
- Corresponding author
- Lotter, William
- ORCID
- Venue
- Nature Medicine
- Published
- 2021-01-11
- Volume
- 27
- Issue
- 2
- Pages
- 244-249
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC9426656
- OpenAlex
- W2994949497
- Cited by
- 423
- References
- 50
- Citations by year
- 2026: 59 · 2025: 81 · 2024: 91 · 2023: 93 · 2022: 59 · 2021: 39 · 2020: 1
- FWCI
- 24.82
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- The Journal of the American Medical Association (JAMA) Network (American Medical Association) (submittedVersion)
- arXiv (Cornell University)
- Funders
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (1R44CA240022, 1R37CA240403-01A1)
- National Science Foundation (1938387)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Digital Radiography and Breast Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- breast cancer detection, mammography, digital breast tomosynthesis, annotation efficiency, multiple instance learning, interpretability, mammography screening, computer-aided detection
- MeSH terms
- Deep Learning, Adult, Breast, Breast Neoplasms, Female, Humans, Mammography, Middle Aged, Early Detection of Cancer
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