Detecting outlying subjects in high-dimensional neuroimaging datasets with regularized minimum covariance determinant

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

Volumn 6893 LNCS, Issue PART 3, 2011, Pages 264-271

  Fritsch, Virgile ^a,b   Varoquaux, Gael ^a,b,c   Thyreau, Benjamin ^b   Poline, Jean Baptiste ^a,b   Thirion, Bertrand ^a,b  

^a INRIA SACLAY   (France)

^b DIF   (France)

^c INSERM   (France)

Author keywords

fMRI; Minimum Covariance Determinant; neuroimaging; Outlier detection; regularization; robust estimation

Indexed keywords

BRAIN MAPPING; CLINICAL RESEARCH; MEDICAL COMPUTING; NEUROIMAGING;

FMRI; MINIMUM COVARIANCE DETERMINANT; OUTLIER DETECTION; REGULARIZATION; ROBUST ESTIMATION;

POPULATION STATISTICS;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; AUTOMATION; BRAIN MAPPING; COMPUTER PROGRAM; DIAGNOSTIC IMAGING; HUMAN; IMAGE PROCESSING; METHODOLOGY; NORMAL DISTRIBUTION; NUCLEAR MAGNETIC RESONANCE IMAGING; RECEIVER OPERATING CHARACTERISTIC; REPRODUCIBILITY; STATISTICAL MODEL;

ALGORITHMS; AUTOMATION; BRAIN MAPPING; DIAGNOSTIC IMAGING; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, STATISTICAL; NORMAL DISTRIBUTION; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; ROC CURVE; SOFTWARE;

EID: 82255164557     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23626-6_33     Document Type: Conference Paper

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