Prostate histopathology: Learning tissue component histograms for cancer detection and classification

IEEE Transactions on Medical Imaging

Volumn 32, Issue 10, 2013, Pages 1804-1818

  Gorelick, Lena ^a   Veksler, Olga ^b   Gaed, Mena ^a,c   Gomez, Jose A ^b   Moussa, Madeleine ^b   Bauman, Glenn ^b,c   Fenster, Aaron ^a,b,c   Ward, Aaron D ^b  

^a ROBARTS RESEARCH INSTITUTE   (Canada)

^b UNIVERSITY OF WESTERN ONTARIO   (Canada)

^c LAWSON HEALTH RESEARCH INSTITUTE   (Canada)

Author keywords

Automated prostate cancer detection; Cancer grading; Digital pathology image analysis; Machine learning; Quantitative pathology; Superpixels

Indexed keywords

ADJUVANT TREATMENT; CLASSIFICATION TASKS; DIGITAL PATHOLOGIES; IMAGE PARTITIONING; PROSTATE CANCER DETECTION; QUALITATIVE ASSESSMENTS; RADICAL PROSTATECTOMY; SUPERPIXELS;

ADAPTIVE BOOSTING; DISEASES; GRADING; LEARNING SYSTEMS; PATHOLOGY; UROLOGY;

TISSUE;

EOSIN; HEMATOXYLIN;

ACCURACY; ARTICLE; CANCER CLASSIFICATION; CANCER DIAGNOSIS; CANCER GRADING; CANCER PATIENT; GLEASON SCORE; HISTOPATHOLOGY; HUMAN; HUMAN TISSUE; IMAGE ANALYSIS; MALE; PROSTATE; PROSTATE CANCER; TISSUE SECTION;

ARTIFICIAL INTELLIGENCE; HISTOLOGICAL TECHNIQUES; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; PROGNOSIS; PROSTATE; PROSTATECTOMY; PROSTATIC NEOPLASMS;

EID: 84885143428     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2013.2265334     Document Type: Article

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