Massive-training artificial neural network coupled with laplacian-eigenfunction-based dimensionality reduction for computer-aided detection of polyps in CT colonography

IEEE Transactions on Medical Imaging

Volumn 29, Issue 11, 2010, Pages 1907-1917

  Suzuki, Kenji ^a   Zhang, Jun ^a   Xu, Jianwu ^a  

^a UNIVERSITY OF CHICAGO   (United States)

Author keywords

Computer aided diagnosis (CAD); nonlinear dimension reduction; pixel based machine learning; virtual colonoscopy

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BASIC PROPERTIES; CHARACTERISTIC CURVE; CLASSIFICATION PERFORMANCE; COMPUTER-AIDED DETECTION; CT COLONOGRAPHY; DATA SETS; DIMENSION REDUCTION; DIMENSION REDUCTION METHOD; DIMENSIONALITY REDUCTION; EIGEN FUNCTION; HIGH DIMENSIONALITY; HIGH SENSITIVITY; INPUT VECTOR; LAPLACIANS; MACHINE-LEARNING; NONLINEAR DIMENSION REDUCTION; P-VALUES; PRONE POSITION; SINGLE SOURCE; STATISTICALLY SIGNIFICANT DIFFERENCE; SUBVOLUMES; TEST SETS; TIME ISSUES; TRAINING SETS; VIRTUAL COLONOSCOPY;

COMPUTERIZED TOMOGRAPHY; EIGENVALUES AND EIGENFUNCTIONS; ENDOSCOPY; LAPLACE TRANSFORMS; LEARNING SYSTEMS; MAMMOGRAPHY; NEURAL NETWORKS; PIXELS; TEACHING;

COMPUTER AIDED DIAGNOSIS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COLON POLYP; COMPUTED TOMOGRAPHIC COLONOGRAPHY; COMPUTER ASSISTED DIAGNOSIS; HUMAN; IMAGE QUALITY; METHODOLOGY; RADIOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; COLONIC POLYPS; COLONOGRAPHY, COMPUTED TOMOGRAPHIC; HUMANS; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 78149266353     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2010.2053213     Document Type: Article

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