Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study

Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study

Abstract Background To determine if mammographic features from deep learning networks can be applied in breast cancer to identify groups at interval invasive cancer risk due to masking beyond using traditional breast density measures. Methods Full-field digital screening mammograms acquired in our c...

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Bibliographic Details
Main Authors: Benjamin Hinton, Lin Ma, Amir Pasha Mahmoudzadeh, Serghei Malkov, Bo Fan, Heather Greenwood, Bonnie Joe, Vivian Lee, Karla Kerlikowske, John Shepherd
Format: Article
Language:English
Published: BMC 2019-06-01
Series:Cancer Imaging
Subjects:
Breast Cancer
Masking
Mammography
Interval Cancer
Deep learning
Transfer learning
Online Access:http://link.springer.com/article/10.1186/s40644-019-0227-3

Internet

http://link.springer.com/article/10.1186/s40644-019-0227-3

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