Predicting cognitive data from medical images using sparse linear regression

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

Volumn 7917 LNCS, Issue , 2013, Pages 86-97

  Kandel, Benjamin M ^a,d   Wolk, David A ^b   Gee, James C ^c,d   Avants, Brian ^c,d  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c UNIVERSITY OF PENNSYLVANIA   (United States)

^d Penn Image Computing and Science Laboratory (PICSL)   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COGNITIVE FUNCTIONS; CONTINUOUS VARIABLES; HIGH-DIMENSIONAL; INTERPRETABILITY; MACHINE LEARNING TECHNIQUES; OPTIMIZATION ALGORITHMS; SPARSE LINEAR REGRESSIONS; SPARSE REGRESSION;

ALGORITHMS; CONJUGATE GRADIENT METHOD; DATA PROCESSING; LEARNING SYSTEMS;

MEDICAL IMAGING;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; MILD COGNITIVE IMPAIRMENT; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PROGNOSIS; REGRESSION ANALYSIS; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; BRAIN; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LINEAR MODELS; MAGNETIC RESONANCE IMAGING; MILD COGNITIVE IMPAIRMENT; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; PROGNOSIS; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84901249471     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-38868-2_8     Document Type: Conference Paper

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