A hierarchical unsupervised spectral clustering scheme for detection of prostate cancer from Magnetic Resonance Spectroscopy (MRS)

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

Volumn 4792 LNCS, Issue PART 2, 2007, Pages 278-286

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

^a RUTGERS UNIVERSITY   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; DIAGNOSIS; LEARNING SYSTEMS; ONCOLOGY;

PROSTATE CANCER; SEXTANT LOCATION; SPECTRAL CLUSTERING;

MAGNETIC RESONANCE SPECTROSCOPY;

TUMOR MARKER; TUMOR PROTEIN;

ADENOCARCINOMA; ARTICLE; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; COMPUTER ASSISTED DIAGNOSIS; EVALUATION; HUMAN; MALE; METABOLISM; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PROSTATE TUMOR; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ADENOCARCINOMA; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; MAGNETIC RESONANCE IMAGING; MAGNETIC RESONANCE SPECTROSCOPY; MALE; NEOPLASM PROTEINS; PROSTATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TUMOR MARKERS, BIOLOGICAL;

EID: 79551684061     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-75759-7_34     Document Type: Conference Paper

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