On the classification of prostate carcinoma with methods from spatial statistics

IEEE Transactions on Information Technology in Biomedicine

Volumn 11, Issue 4, 2007, Pages 406-414

  Wittke, Claudia ^a   Mayer, Johannes ^b   Schweiggert, Franz ^b  

^a Capgemini Sd and M   (Germany)

^b UNIVERSITY OF ULM   (Germany)

Author keywords

Automatic classification; Gleason grading; Interrater reliability; Morphological characteristics; Prostatic adenocarcinoma; Spatial statistics

Indexed keywords

EULER NUMBER; GLEASON GRADING; PROSTATE CARCINOMA;

HISTOLOGY; MORPHOLOGY; ONCOLOGY; PATTERN RECOGNITION; STATISTICS; TUMORS;

PATHOLOGY;

ADENOCARCINOMA; ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; CANCER STAGING; CLASSIFICATION; COMPUTER ASSISTED DIAGNOSIS; HOSPITALIZATION; HUMAN; MALE; METHODOLOGY; PATHOLOGY; PROSTATE TUMOR; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS;

ADENOCARCINOMA; ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA INTERPRETATION, STATISTICAL; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; NEOPLASM STAGING; PROSTATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SEVERITY OF ILLNESS INDEX;

EID: 34547100304     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2006.888703     Document Type: Article

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