Cited by 423AI in cancer detectionDigital Radiography and Breast ImagingRadiomics and Machine Learning in Medical Imaging

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Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach

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Lotter, William, Diab, Abdul Rahman, Haslam, Bryan, Kim, Jiye G., Grisot, Giorgia, Wu, Eric, Wu, Kevin, Onieva, Jorge Onieva, Boyer, Yun, Boxerman, Jerrold L., Wang, Meiyun, Bandler, Mack, Vijayaraghavan, Gopal R., Gregory Sorensen, A.·2021·Nature Medicine

DOI 10.1038/s41591-020-01174-9PMC PMC9426656

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Abstract

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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Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21492 · Cited by 84,109
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems, Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 44,708
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Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988) · Biometrics · doi:10.2307/2531595 · Cited by 22,649
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143
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International evaluation of an AI system for breast cancer screening
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1799-6 · Cited by 3,287

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Saadh, M. J., Hussain, Q. M., Albadr, R. J., Doshi, H., Rekha, M. M., Kundlas, M., Pal, A., Rizaev, J. A., Taher, W. M., Alwan, M., et al. (2025) · Journal of X-Ray Science and Technology · doi:10.1177/08953996251351624 · Cited by 4
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Sreeja, G. B., Sudha, S., & Mariapushpam, I. T. (2025) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001425520226 · Cited by 1
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Xie, W., Liu, Z., Tian, J., & Liu, J. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108554 · Cited by 1
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John, M., & Mina, J. (2025) · Journal of Cancer Treatment and Research · doi:10.11648/j.jctr.20251303.14 · Cited by 1
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Kumar, S., Malviya, R., Sridhar, S. B., Shareef, J., & Wadhwa, T. (2025) · Anti-Inflammatory & Anti-Allergy Agents in Medicinal Chemistry · doi:10.2174/0118715230389406250906142050 · Cited by 1
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Rao, S. S., & Vidya, R. (2025) · British Journal of Hospital Medicine · doi:10.12968/hmed.2024.0786 · Cited by 1

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: Lotter, William
Diab, Abdul Rahman
Haslam, Bryan
Kim, Jiye G.
Grisot, Giorgia
Wu, Kevin
Onieva, Jorge Onieva
Boxerman, Jerrold L.
Wang, Meiyun
Gregory Sorensen, A.
Venue: Nature Medicine
Publisher: Springer Science and Business Media LLC
Published: 2021-01-11
Volume: 27
Issue: 2
Pages: 244-249
ISSN: 1078-8956 (ISSN-L)
1546-170X
DOI: 10.1038/s41591-020-01174-9
PMC: PMC9426656
OpenAlex: W2994949497
Cited by: 423
References: 50
Citations by year: 2026: 59 · 2025: 81 · 2024: 91 · 2023: 93 · 2022: 59 · 2021: 39
FWCI: 24.82
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
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 › Radiology, Nuclear Medicine and Imaging › 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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Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach

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