Machine learning approaches to analyze histological images of tissues from radical prostatectomies

Computerized Medical Imaging and Graphics

Volumn 46, Issue , 2015, Pages 197-208

  Gertych, Arkadiusz ^a   Ing, Nathan ^a   Ma, Zhaoxuan ^a   Fuchs, Thomas J ^b,c   Salman, Sadri ^a   Mohanty, Sambit ^a   Bhele, Sanica ^a   Velásquez Vacca, Adriana ^a   Amin, Mahul B ^a   Knudsen, Beatrice S ^a  

^a CEDARS SINAI MEDICAL CENTER   (United States)

^b CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^c MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

Author keywords

Image analysis; Machine learning; Prostate cancer; Tissue classification; Tissue quantification

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); CURRICULA; DECISION TREES; DISEASES; GRAPHIC METHODS; HISTOLOGY; IMAGE ANALYSIS; IMAGE SEGMENTATION; LEARNING SYSTEMS; MOLECULAR BIOLOGY; PIXELS; SUPPORT VECTOR MACHINES; TISSUE; TISSUE ENGINEERING; TUMORS; UROLOGY;

CLASSIFICATION APPROACH; COMPUTERIZED EVALUATION; LOCAL BINARY PATTERNS; MACHINE LEARNING APPROACHES; PIXEL BASED CLASSIFICATIONS; PROSTATE CANCERS; RADICAL PROSTATECTOMY; TISSUE CLASSIFICATION;

IMAGE CLASSIFICATION;

ARTICLE; CANCER DIAGNOSIS; CANCER SURGERY; CLINICAL ARTICLE; CONTROLLED STUDY; HISTOGRAM; HISTOLOGY; HUMAN; HUMAN TISSUE; IMAGE ANALYSIS; MALE; MEASUREMENT ACCURACY; PREDICTION; PRIORITY JOURNAL; PROSTATE; PROSTATE CANCER; PROSTATECTOMY; RANDOM FOREST; SUPPORT VECTOR MACHINE; ALGORITHM; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; EPITHELIUM CELL; IMAGE ENHANCEMENT; MACHINE LEARNING; MICROSCOPY; PATHOLOGY; PROCEDURES; PROSTATIC NEOPLASMS; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STROMA CELL; TREATMENT OUTCOME;

ALGORITHMS; EPITHELIAL CELLS; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MACHINE LEARNING; MALE; MICROSCOPY; PATTERN RECOGNITION, AUTOMATED; PROSTATECTOMY; PROSTATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; STROMAL CELLS; TREATMENT OUTCOME;

EID: 84983119355     PISSN: 08956111     EISSN: 18790771     Source Type: Journal    
DOI: 10.1016/j.compmedimag.2015.08.002     Document Type: Article

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