Implementing machine learning in radiology practice and research

American Journal of Roentgenology

Volumn 208, Issue 4, 2017, Pages 754-760

  Kohli, Marc ^a   Prevedello, Luciano M ^b   Filice, Ross W ^c   Geis, J Raymond ^d  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b THE OHIO STATE UNIVERSITY WEXNER MEDICAL CENTER   (United States)

^c GEORGETOWN UNIVERSITY   (United States)

^d UNIVERSITY OF COLORADO SCHOOL OF MEDICINE   (United States)

Author keywords

Artificial intelligence; Imaging; Informatics; Machine learning; Statistics

Indexed keywords

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; CLINICAL PRACTICE; CLINICAL RESEARCH; COMPUTER AIDED DESIGN; COMPUTER ANALYSIS; CONVOLUTIONAL NEURAL NETWORK; IMAGE ANALYSIS; IMAGE PROCESSING; LEGAL ASPECT; MACHINE LEARNING; PRACTICE GUIDELINE; RADIOLOGIST; RADIOLOGY; REVIEW; SOFTWARE; STATISTICAL ANALYSIS; SUPPORT VECTOR MACHINE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; HUMAN; IMAGE ENHANCEMENT; MEDICAL RESEARCH; ORGANIZATION AND MANAGEMENT; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; UNITED STATES;

ALGORITHMS; BIOMEDICAL RESEARCH; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MACHINE LEARNING; PATTERN RECOGNITION, AUTOMATED; PRACTICE PATTERNS, PHYSICIANS'; RADIOLOGY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; UNITED STATES;

EID: 85016471320     PISSN: 0361803X     EISSN: 15463141     Source Type: Journal    
DOI: 10.2214/AJR.16.17224     Document Type: Review

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