Assessment of Machine Learning of Breast Pathology Structures for Automated Differentiation of Breast Cancer and High-Risk Proliferative Lesions.

Journal: JAMA network open

Published Date: Aug 2, 2019

Abstract

IMPORTANCE: Following recent US Food and Drug Administration approval, adoption of whole slide imaging in clinical settings may be imminent, and diagnostic accuracy, particularly among challenging breast biopsy specimens, may benefit from computerized diagnostic support tools.

Authors

Ezgi Mercan

Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle.
Sachin Mehta

Department of Electrical and Computer Engineering, University of Washington, Seattle.
Jamen Bartlett

University of Vermont Medical Center, Burlington.
Linda G Shapiro

Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle.
Donald L Weaver

Department of Pathology, University of Vermont, Burlington, VT, USA.
Joann G Elmore

Department of Medicine, University of Washington School of Medicine, Seattle.

Keywords

Biopsy Breast Neoplasms Carcinoma, Ductal Carcinoma, Intraductal, Noninfiltrating Female Humans Machine Learning Neural Networks, Computer Reference Standards Registries Sensitivity and Specificity

External Resources

View on PubMed Access via DOI PubMed (31397859)

Assessment of Machine Learning of Breast Pathology Structures for Automated Differentiation of Breast Cancer and High-Risk Proliferative Lesions.

Abstract

Authors

Keywords

External Resources

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