Evaluating stability of histomorphometric features across scanner and staining variations: Prostate cancer diagnosis from whole slide images

Journal of Medical Imaging

Volumn 3, Issue 4, 2016, Pages

  Leo, Patrick ^a   Lee, George ^a   Shih, Natalie N C ^b   Elliott, Robin ^c   Feldman, Michael D ^b   Madabhushi, Anant ^a  

^a Case Western Reserve University   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c CASE WESTERN RESERVE UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

digital pathology; feature stability; machine learning; prognosis; prostate cancer; quantitative histomorphometry; site variation; stain variability

Indexed keywords

ARTICLE; ARTIFACT; BENCHMARKING; CANCER DIAGNOSIS; CANCER PATIENT; CANCER PROGNOSIS; CLASSIFICATION ALGORITHM; COLOR; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DIGITAL IMAGING; DIGITAL SLIDE SCANNER; EXTRACTION; HISTOGRAM; HUMAN; HUMAN TISSUE; IMAGE ANALYSIS; LATENT INSTABILITY SCORE; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MORPHOMETRICS; MULTICENTER STUDY; PROSTATE CANCER; QUANTITATIVE DIAGNOSIS; QUANTITATIVE HISTOMORPHOMETRY; RECEIVER OPERATING CHARACTERISTIC; SCORING SYSTEM; WHOLE SLIDE SCANNER;

EID: 84994473894     PISSN: 23294302     EISSN: 23294310     Source Type: Journal    
DOI: 10.1117/1.JMI.3.4.047502     Document Type: Article

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