Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning

Journal of Biomedical Optics

Volumn 22, Issue 3, 2017, Pages

  Nguyen, Tan H ^a   Sridharan, Shamira ^a   MacIas, Virgilia ^b   Kajdacsy Balla, Andre ^a,b   Melamed, Jonathan ^c   Do, Minh N ^d   Popescua, Gabriel ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

^c NEW YORK UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d University of Illinois at Urbana Champaign   (United States)

Author keywords

holography; machine learning; microscopy; prostate cancer diagnosis; quantitative phase imaging

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION TREES; DISEASES; GRADING; HOLOGRAPHIC INTERFEROMETRY; HOLOGRAPHY; LEARNING SYSTEMS; MICROSCOPIC EXAMINATION; TISSUE; TISSUE ENGINEERING; UROLOGY;

INTEROBSERVER VARIABILITY; MORPHOLOGICAL FEATURES; NANOSCALE ARCHITECTURES; PROSTATE CANCERS; QUANTITATIVE INFORMATION; QUANTITATIVE PHASE IMAGING; RANDOM FOREST CLASSIFIER; RECEIVER OPERATING CURVES;

LEARNING ALGORITHMS;

ALGORITHM; BIOPSY; CANCER GRADING; CLASSIFICATION; DIAGNOSTIC IMAGING; HUMAN; MACHINE LEARNING; MALE; PROCEDURES; PROSTATE TUMOR;

ALGORITHMS; BIOPSY; HUMANS; MACHINE LEARNING; MALE; NEOPLASM GRADING; PROSTATIC NEOPLASMS;

EID: 85017036127     PISSN: 10833668     EISSN: 15602281     Source Type: Journal    
DOI: 10.1117/1.JBO.22.3.036015     Document Type: Article

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