Consensus-Locally Linear Embedding (C-LLE): Application to prostate cancer detection on magnetic resonance spectroscopy

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5242 LNCS, Issue PART 2, 2008, Pages 330-338

  Tiwari, Pallavi ^a   Rosen, Mark ^b   Madabhushi, Anant ^a  

^a RUTGERS UNIVERSITY   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIMENSIONALITY REDUCTION; EMBEDDINGS; MAGNETIC RESONANCE; MAGNETIC RESONANCE SPECTROSCOPY; MEDICAL COMPUTING; MEDICAL IMAGING; UROLOGY;

HIGH-DIMENSIONAL FEATURE SPACE; LOCALLY LINEAR EMBEDDING; LOW DIMENSIONAL EMBEDDING; LOW-DIMENSIONAL REPRESENTATION; MAGNETIC RESONANCE SPECTROSCOPIES (MRS); NONLINEAR DIMENSIONALITY REDUCTION; PROSTATE CANCER DETECTION; STATE-OF-THE-ART SCHEME;

DISEASES;

TUMOR MARKER;

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; HUMAN; MALE; METABOLISM; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PROSTATE TUMOR; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

ALGORITHMS; COMPUTER SIMULATION; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; LINEAR MODELS; MAGNETIC RESONANCE SPECTROSCOPY; MALE; MODELS, BIOLOGICAL; PROSTATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TUMOR MARKERS, BIOLOGICAL;

EID: 58949098056     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85990-1_40     Document Type: Conference Paper

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