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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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
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
Focal Loss for Dense Object Detection
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
Untitled work
(2018) · Cited by 3,510
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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Vanak, J. (2022) · Science Insights · doi:10.15354/si.22.re068 · Cited by 14
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Remaigui, I., Kahloul, L., & Benharzallah, S. (2022) · International Multi-Conference on Systems, Signals & Devices (SSD) · doi:10.1109/ssd54932.2022.9955773 · Cited by 10
MultiBCD: A multimodal model that simulates the human diagnostic process for automated breast cancer detection
Du, J., Zhang, Z., & Tao, W. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107410 · Cited by 10
Event-Level Identification of Sleep Apnea Using FMCW Radar
Zhang, H., Bo, S., Zhang, X., Wang, P., Du, L., Li, Z., Wu, P., Chen, X., Jiang, L., & Fang, Z. (2025) · Bioengineering · doi:10.3390/bioengineering12040399 · Cited by 10
Paradigm-Shifting Attention-Based Hybrid View Learning for Enhanced Mammography Breast Cancer Classification With Multi-Scale and Multi-View Fusion
Zhao, H., Zhang, C., Chen, J., Li, Z., Wang, F., & Gao, S. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3569726 · Cited by 11
Ipsilateral Lesion Detection Refinement for Tomosynthesis
Ren, Y., Liu, X., Ge, J., Liang, Z., Xu, X., Grimm, L. J., Go, J., Marks, J. R., & Lo, J. Y.-C. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3280135 · Cited by 11
Deep learning model for predicting the presence of stromal invasion of breast cancer on digital breast tomosynthesis
Shimokawa, D., Takahashi, K., Oba, K., Takaya, E., Usuzaki, T., Kadowaki, M., Kawaguchi, K., Adachi, M., Kaneno, T., Fukuda, T., et al. (2023) · Radiological Physics and Technology · doi:10.1007/s12194-023-00731-4 · Cited by 10
DeepNCI: DFT Noncovalent Interaction Correction with Transferable Multimodal Three-Dimensional Convolutional Neural Networks
Li, W., Wang, D., Yang, Z.-R., Zhang, H., Hu, L., & Chen, G. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01305 · Cited by 11
Breast cancer detection using machine learning in digital mammography and breast tomosynthesis: A systematic review
Malliori, A., & Pallikarakis, N. E. (2022) · Health and Technology · doi:10.1007/s12553-022-00693-4 · Cited by 11
Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammography
Woo, M., Zhang, L., Brown-Mulry, B., Hwang, I., Gichoya, J. W., Gastounioti, A., Banerjee, I., Seyyed-Kalantari, L., & Trivedi, H. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000811 · Cited by 12
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Varela, S., Zheng, X., Njuguna, J. N., Sacks, E. J., Allen, D. P., Ruhter, J., & Leakey, A. D. B. (2025) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiaf132 · Cited by 13
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Lehman, C. D., & Wu, S. (2021) · Nature Cancer · doi:10.1038/s43018-021-00307-4 · Cited by 9
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Bhalla, D., Rangarajan, K., Chandra, T., Banerjee, S., & Arora, C. (2023) · Indian journal of radiology and imaging - new series/Indian journal of radiology and imaging/Indian Journal of Radiology & Imaging · doi:10.1055/s-0043-1775737 · Cited by 9
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Anand, A., Jung, S., & Lee, S. (2024) · Sensors · doi:10.3390/s24216890 · Cited by 9
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Chua, B. N., Thng, D. K. H., Toh, T. B., & Ho, D. (2026) · Communications Medicine · doi:10.1038/s43856-025-01342-3 · Cited by 9
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Liang, Z., Niu, Q., Wang, J., Han, C., Li, Q., Wu, Y., Zhu, B., Han, X., Wang, Z., Wang, X., et al. (2026) · Nature Health · doi:10.1038/s44360-026-00055-8 · Cited by 9
Recent advancements of deep learning in detecting breast cancer: a survey
Gautam, A. (2022) · Multimedia Systems · doi:10.1007/s00530-022-01028-z · Cited by 8
GravityNet for end-to-end small lesion detection
Russo, C., Bria, A., & Marrocco, C. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102842 · Cited by 12
Recent Advancements of Nanomedicine in Breast Cancer Surgery
Meng, X., Wang, X., Zhang, Z., Song, L., & Chen, J. (2024) · International Journal of Nanomedicine · doi:10.2147/ijn.s494364 · Cited by 9
Harnessing the potential of long non-coding RNAs in breast cancer: from etiology to treatment resistance and clinical applications
Wang, Y., Bu, N., Luan, X.-F., Song, Q.-Q., Ma, B.-F., Hao, W., Yan, J., Wang, L., Zheng, X.-L., & Maimaitiyiming, Y. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1337579 · Cited by 8
Clinical Application of Artificial Intelligence in Digital Breast Tomosynthesis
Chang, J. M., Lee, W., & Bahl, M. (2025) · Journal of the Korean Society of Radiology · doi:10.3348/jksr.2025.0011 · Cited by 7
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Wulamu, A., Luo, J., Chen, S., Zheng, H., Wang, T., Yang, R., Jiao, L., & Zhang, T. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107871 · Cited by 7
Diagnosis model of early Pneumocystis jirovecii pneumonia based on convolutional neural network: a comparison with traditional PCR diagnostic method
Li, Y., Liu, H., Lv, Q., & Long, J. (2024) · BMC Pulmonary Medicine · doi:10.1186/s12890-024-02987-x · Cited by 7
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Jha, G., Jha, A., & John, E. B. (2024) · IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) · doi:10.1109/mwscas60917.2024.10658807 · Cited by 7
Domain generalization for mammographic image analysis with contrastive learning
Li, Z., Cui, Z., Zhang, L., Wang, S., Lei, C., Ouyang, X., Chen, D., Zhao, X., Liu, C., Liu, Z., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109455 · Cited by 7
Breast cancer detection and classification with digital breast tomosynthesis: a two-stage deep learning approach
Alashban, Y. I. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.242923 · Cited by 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: 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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