Deep learning in mammography diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer

Investigative Radiology

Volumn 52, Issue 7, 2017, Pages 434-440

  Becker, Anton S ^a   Marcon, Magda ^a   Ghafoor, Soleen ^a   Wurnig, Moritz C ^a   Frauenfelder, Thomas ^a   Boss, Andreas ^a  

^a University Hospital Zurich Institut fur Diagnostische Radiologie   (Switzerland)

Author keywords

Artificial intelligence; Artificial neural network; Breast cancer; Deep learning; Diagnostic accuracy; Machine learning; Mammography

Indexed keywords

ADULT; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; BREAST CARCINOMA; BREAST DENSITY; COLLOID CARCINOMA; CONTROLLED STUDY; CORRELATION COEFFICIENT; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; HISTOLOGY; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGING SOFTWARE; INTRADUCTAL CARCINOMA; LOBULAR CARCINOMA; MAJOR CLINICAL STUDY; MAMMOGRAPHY; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; RETROSPECTIVE STUDY; SENSITIVITY AND SPECIFICITY; BREAST; BREAST TUMOR; DIAGNOSTIC IMAGING; FEMALE; FOLLOW UP; MACHINE LEARNING; MIDDLE AGED; PREVALENCE; PROCEDURES; REPRODUCIBILITY; VERY ELDERLY;

ADULT; AGED; AGED, 80 AND OVER; BREAST; BREAST NEOPLASMS; FEMALE; FOLLOW-UP STUDIES; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MACHINE LEARNING; MAMMOGRAPHY; MIDDLE AGED; PREVALENCE; REPRODUCIBILITY OF RESULTS; RETROSPECTIVE STUDIES; ROC CURVE;

EID: 85013092765     PISSN: 00209996     EISSN: 15360210     Source Type: Journal    
DOI: 10.1097/RLI.0000000000000358     Document Type: Article

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