Assessment of Machine Learning of Breast Pathology Structures for Automated Differentiation of Breast Cancer and High-Risk Proliferative Lesions-Reference-Cited by-同舟云学术

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

Published:2019-08-09 Issue:8 Volume:2 Page:e198777
ISSN:2574-3805
Container-title:JAMA Network Open
language:en
Short-container-title:JAMA Netw Open

Author:

Mercan Ezgi¹²,Mehta Sachin³,Bartlett Jamen⁴⁵,Shapiro Linda G.¹,Weaver Donald L.⁶,Elmore Joann G.⁷

Affiliation:

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

2. nowwith Seattle Children’s Hospital, Seattle, Washington

3. Department of Electrical and Computer Engineering, University of Washington, Seattle

4. University of Vermont Medical Center, Burlington

5. now with Southern Ohio Pathology Consultants, Cincinnati, Ohio

6. Department of Pathology and University of Vermont Cancer Center, Larner College of Medicine, University of Vermont, Burlington

7. Division of General Internal Medicine and Health Services Research, Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles

Publisher

American Medical Association (AMA)

Subject

General Medicine

Link

https://jamanetwork.com/journals/jamanetworkopen/article-pdf/2/8/e198777/17768791/mercan_2019_oi_190350.pdf

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3. Accuracy of distinguishing atypical ductal hyperplasia from ductal carcinoma in situ with convolutional neural network-based machine learning approach using mammographic image data.;Ha;AJR Am J Roentgenol,2019

4. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer.;Ehteshami Bejnordi;JAMA,2017

5. Breast cancer histopathological image classification: a deep learning approach.;Motlagh;bioRxiv,2018

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