High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks

Journal of Digital Imaging

Volumn 30, Issue 1, 2017, Pages 95-101

  Rajkomar, Alvin ^a,b   Lingam, Sneha ^b   Taylor, Andrew G ^a   Blum, Michael ^b   Mongan, John ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Artificial neural networks; Chest radiographs; Computer vision; Convolutional neural network; Deep learning; Machine learning; Radiography

Indexed keywords

ARTIFICIAL INTELLIGENCE; BINS; CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; COMPUTER VISION; CONVOLUTION; LEARNING SYSTEMS; NEURAL NETWORKS; RADIATION; RADIOGRAPHY; RADIOLOGY; THROUGHPUT;

BINARY CLASSIFICATION METHODS; CHEST RADIOGRAPHS; COMPUTER VISION TECHNIQUES; CONVOLUTIONAL NETWORKS; CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING; RADIOLOGICAL IMAGES; VALIDATION SETS;

IMAGE CLASSIFICATION;

ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; HUMAN; RADIOGRAPHY; RANDOMIZATION; RETROSPECTIVE STUDY; STATISTICS AND NUMERICAL DATA; THORAX RADIOGRAPHY;

HUMANS; NEURAL NETWORKS (COMPUTER); RADIOGRAPHY; RADIOGRAPHY, THORACIC; RANDOM ALLOCATION; RETROSPECTIVE STUDIES;

EID: 84991063753     PISSN: 08971889     EISSN: 1618727X     Source Type: Journal    
DOI: 10.1007/s10278-016-9914-9     Document Type: Article

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