Classification of incidental carcinoma of the prostate using learning vector quantization and support vector machines

Cellular Oncology

Volumn 26, Issue 1-2, 2004, Pages 45-55

  Mattfeldt, Torsten ^a,b   Trijic, Danilo ^a   Gottfried, Hans Werner ^a   Kestler, Hans A ^a  

^a UNIVERSITY OF ULM   (Germany)

^b Oberer Eselsberg M23 ^*  (Germany)

Author keywords

Artificial neural networks; Bioinformatics; Classification; Immunohistochemistry; Incidental carcinoma; Learning vector quantization; Logistic regression; Pathology; Pattern recognition; Prostatic cancer; Stereology; Support vector machine

Indexed keywords

PROSTATE SPECIFIC ANTIGEN; PROTEIN P53;

ADULT; AGE; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOINFORMATICS; CANCER CLASSIFICATION; CELL PROLIFERATION; CLINICAL FEATURE; DIAGNOSTIC ACCURACY; GENE OVEREXPRESSION; HISTOPATHOLOGY; HUMAN; HUMAN TISSUE; IMMUNOHISTOCHEMISTRY; INCIDENTAL FINDING; LEARNING VECTOR QUANTIZATION; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MALE; MULTIVARIATE ANALYSIS; PREDICTION; PREOPERATIVE EVALUATION; PRIORITY JOURNAL; PROGNOSIS; PROSTATE ADENOCARCINOMA; PROSTATE CARCINOMA; PROSTATE EPITHELIUM; SCORING SYSTEM; SENSITIVITY AND SPECIFICITY; STEREOLOGY; SUPPORT VECTOR MACHINE; TUMOR SUPPRESSOR GENE; TUMOR VASCULARIZATION;

ADENOCARCINOMA; AGED; ARTIFICIAL INTELLIGENCE; CELL PROLIFERATION; EPITHELIAL CELLS; HUMANS; IMAGE CYTOMETRY; IMMUNOHISTOCHEMISTRY; LOGISTIC MODELS; MALE; NEOVASCULARIZATION, PATHOLOGIC; NEURAL NETWORKS (COMPUTER); PROSTATE; PROSTATE-SPECIFIC ANTIGEN; PROSTATIC NEOPLASMS; TUMOR SUPPRESSOR PROTEIN P53;

EID: 4243076204     PISSN: 15705870     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

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