Temporally-constrained group sparse learning for longitudinal data analysis

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

Volumn 7512 LNCS, Issue , 2012, Pages 264-271

  Zhang, Daoqiang ^a,b   Liu, Jun ^c   Shen, Dinggang ^a  

^a UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^b NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

^c SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN; BRAIN MAPPING; DIAGNOSIS; MEDICAL COMPUTING; REGRESSION ANALYSIS;

CROSS SECTIONAL ANALYSIS; DISEASE PROGRESSION; GROUP SPARSITIES; LONGITUDINAL ANALYSIS; LONGITUDINAL DATA; OBJECTIVE FUNCTIONS; REGULARIZATION TERMS; SPARSE LINEAR REGRESSIONS;

LEARNING SYSTEMS;

AGING; ALGORITHM; ALZHEIMER DISEASE; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; LONGITUDINAL STUDY; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

AGING; ALGORITHMS; ALZHEIMER DISEASE; ARTIFICIAL INTELLIGENCE; BRAIN; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LONGITUDINAL STUDIES; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

MLCS; MLOWN;

EID: 84872928144     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33454-2_33     Document Type: Conference Paper

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