Deep learning for identifying radiogenomic associations in breast cancer-Reference-Cited by-同舟云学术

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Deep learning for identifying radiogenomic associations in breast cancer

Published:2019-06 Issue: Volume:109 Page:85-90
ISSN:0010-4825
Container-title:Computers in Biology and Medicine
language:en
Short-container-title:Computers in Biology and Medicine

Author:

Zhu Zhe,Albadawy Ehab,Saha Ashirbani,Zhang Jun,Harowicz Michael R.,Mazurowski Maciej A.

Funder

North Carolina Biotechnology Center

National Institutes of Health

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference29 articles.

1. Triple-negative breast cancer: correlation between MR imaging and pathologic Findings1;Uematsu;Radiology,2009

2. Magnetic resonance imaging features in triple-negative breast cancer: comparison with luminal and HER2-overexpressing tumors;Costantini;Clin. Breast Canc.,2012

3. Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape;Yamamoto;Am. J. Roentgenol.,2012

4. MR imaging features of triple-negative breast cancers;Sung;Breast J.,2013

5. Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility study;Agner;Radiology,2014

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