Semi Supervised Multi Kernel (SeSMiK) graph embedding: Identifying aggressive prostate cancer via magnetic resonance imaging and spectroscopy

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

Volumn 6363 LNCS, Issue PART 3, 2010, Pages 666-673

  Tiwari, Pallavi ^a   Kurhanewicz, John ^b   Rosen, Mark ^c   Madabhushi, Anant ^a  

^a RUTGERS UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; DATA STREAM; DIMENSIONALITY REDUCTION; DISEASE CLASSIFICATION; DISEASE DIAGNOSTICS; GRAPH EMBEDDINGS; HIGH DIMENSIONAL DATA; IN-VIVO; INTEGRATING INFORMATION; LABEL INFORMATION; LOW GRADE; META-CLASSIFIERS; MULTI-KERNEL; MULTI-MODAL; MULTI-MODAL DATA; MULTISCALES; NONLINEAR DIMENSIONALITY REDUCTION; PROSTATE CANCERS; REDUCED SPACE; SEMI-SUPERVISED; UNIMODAL;

CLASSIFIERS; CLUSTERING ALGORITHMS; DATA MINING; DISEASES; KNOWLEDGE REPRESENTATION; MAGNETIC RESONANCE IMAGING; MEDICAL COMPUTING; MEDICAL IMAGING; MODAL ANALYSIS; RESONANCE;

DATA REDUCTION;

TUMOR MARKER;

ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; DIFFUSION WEIGHTED IMAGING; HUMAN; MALE; METABOLISM; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PROSTATE TUMOR; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ARTIFICIAL INTELLIGENCE; DIAGNOSIS, COMPUTER-ASSISTED; DIFFUSION MAGNETIC RESONANCE IMAGING; HUMANS; MAGNETIC RESONANCE SPECTROSCOPY; MALE; PATTERN RECOGNITION, AUTOMATED; PROSTATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TUMOR MARKERS, BIOLOGICAL;

EID: 84870915433     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15711-0_83     Document Type: Conference Paper

References (12)

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