Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer

BMC Bioinformatics

Volumn 13, Issue 1, 2012, Pages

  Doyle, Scott ^a   Feldman, Michael D ^b   Shih, Natalie ^b   Tomaszewski, John ^c   Madabhushi, Anant ^d  

^a Ibris Inc   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c UNIVERSITY AT BUFFALO   (United States)

^d Case Western Reserve University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARCHITECTURAL FEATURES; AUTOMATED CLASSIFICATION; AUTOMATED CLASSIFIERS; BINARY CLASSIFICATION; BINARY CLASSIFIERS; BIOPSY SAMPLES; CLASS LABELS; CONFOUNDER; DIAGNOSTIC ERRORS; DIFFERENT CLASS; DOMAIN KNOWLEDGE; GLEASON GRADING; HIGH GRADES; IMAGE FEATURE SET; INTER-GROUP; INTRA-GROUP; MULTI-CATEGORY CLASSIFICATION; MULTI-CLASS; MULTIPLE CLASS; NORMAL TISSUE; POSITIVE PREDICTIVE VALUES; PROSTATE BIOPSY; PROSTATE CANCERS; TEXTURE FEATURES; TISSUE IMAGES; TISSUE SAMPLES; TISSUE TYPES;

BIOPSY; CLASSIFIERS; DISEASES; DISTRIBUTED COMPUTER SYSTEMS; UROLOGY;

TISSUE;

ARTICLE; CANCER GRADING; CLASSIFICATION; EPITHELIUM; HUMAN; MALE; METHODOLOGY; PATHOLOGY; PROSTATE; PROSTATE TUMOR; PROSTATIC INTRAEPITHELIAL NEOPLASIA;

EPITHELIUM; HUMANS; MALE; NEOPLASM GRADING; PROSTATE; PROSTATIC INTRAEPITHELIAL NEOPLASIA; PROSTATIC NEOPLASMS;

MLCS; MLOWN;

EID: 84867896468     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-13-282     Document Type: Article

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