A statistical approach to set classification by feature selection with applications to classification of histopathology images

Biometrics

Volumn 70, Issue 3, 2014, Pages 536-545

  Jung, Sungkyu ^a   Qiao, Xingye ^b  

^a UNIVERSITY OF PITTSBURGH   (United States)

^b BINGHAMTON UNIVERSITY   (United States)

Author keywords

Bioinformatics; Canonical angles; Discriminant analysis; Hotelling's T square; Multidimensional scaling; Principal component analysis; Set classification

Indexed keywords

DIAGNOSIS; DISCRIMINANT ANALYSIS; FEATURE SELECTION; IMAGE ANALYSIS;

CANONICAL ANGLES; CELL NUCLEUS; CLASSIFICATION TASKS; FEATURES SELECTION; HOTELLING T; HOTELLING T-SQUARE; MULTI-DIMENSIONAL SCALING; PRINCIPAL-COMPONENT ANALYSIS; SET CLASSIFICATION; STATISTICAL APPROACH;

PRINCIPAL COMPONENT ANALYSIS;

ALGORITHM; AUTOMATED PATTERN RECOGNITION; BIOPSY; CELL NUCLEUS; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; HUMAN; IMAGE ENHANCEMENT; LIVER TUMOR; MICROSCOPY; PATHOLOGY; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; BIOPSY; CELL NUCLEUS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LIVER NEOPLASMS; MICROSCOPY; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84927695381     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/biom.12164     Document Type: Article

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