Work

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

Year: 2021

Type: article

Source: Nature Medicine

DOI: 10.1038/s41591-020-01174-9

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

Institutions: Stanford University, Brown University, Rhode Island Hospital, Henan Provincial People's Hospital, Medford Radiology Group, University of Massachusetts Chan Medical School

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: National Science Foundation, U.S. Department of Health and Human Services, National Institutes of Health, National Cancer Institute

Awards: G1676511524, Towards Clinically Effective AI for Screening Mammography, G6783357529, SBIR Phase I: Exploring bias in Deep learning to extend its use to under-represented populations in breast imaging, G7902159274, Artificial Intelligence for Improved Breast Cancer Screening Accuracy: External Validation, Refinement, and Clinical Translation

OpenAlex ID: W2994949497

FWCI: 21.39

Cites: 50

Cited by: 454

Related to: 10