Detecting Cardiovascular Disease from Mammograms with Deep Learning

IEEE Transactions on Medical Imaging

Volumn 36, Issue 5, 2017, Pages 1172-1181

  Wang, Juan ^a   Ding, Huanjun ^b   Bidgoli, Fatemeh Azamian ^b   Zhou, Brian ^b   Iribarren, Carlos ^c,d   Molloi, Sabee ^b   Baldi, Pierre ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c DIVISION OF RESEARCH   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Breast arterial calcification (BAC); coronary artery disease; deep learning; mammography

Indexed keywords

AUTOMATION; BIOMINERALIZATION; BONE; CALCIUM; DEEP LEARNING; MAMMOGRAPHY; NEURAL NETWORKS; RISK ASSESSMENT; X RAY SCREENS;

BREAST ARTERIAL CALCIFICATION (BAC); CARDIO-VASCULAR DISEASE; CARDIOVASCULAR RISK; CONVOLUTIONAL NEURAL NETWORK; CORONARY ARTERY DISEASE; FULL-FIELD DIGITAL MAMMOGRAMS; LEARNING APPROACH; QUANTIFICATION ANALYSIS;

DISEASES;

CALCIUM;

ARTERY CALCIFICATION; ARTICLE; BREAST ARTERIAL CALCIFICATION; BREAST CALCIFICATION; BREAST DENSITY; CARDIOVASCULAR RISK; CORONARY ARTERY DISEASE; FEASIBILITY STUDY; HUMAN; MAJOR CLINICAL STUDY; MAMMOGRAPHY; MAMMOGRAPHY SYSTEM; RADIOLOGIST; RISK ASSESSMENT; ARTIFICIAL NEURAL NETWORK; CALCINOSIS; CARDIOVASCULAR DISEASE; DIAGNOSTIC IMAGING; FEMALE;

CALCINOSIS; CARDIOVASCULAR DISEASES; CORONARY ARTERY DISEASE; FEMALE; HUMANS; MAMMOGRAPHY; NEURAL NETWORKS (COMPUTER);

EID: 85019258762     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2017.2655486     Document Type: Article

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