A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis

NeuroImage

Volumn 100, Issue , 2014, Pages 91-105

  Zhu, Xiaofeng ^a   Suk, Heung Il ^a   Shen, Dinggang ^a,b  

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

^b Korea University   (South Korea)

Author keywords

Alzheimer's disease (AD); Feature selection; Joint sparse learning; Manifold learning; Mild Cognitive Impairment (MCI) conversion

Indexed keywords

ADULT; AGED; ALZHEIMER DISEASE; AMYGDALOID NUCLEUS; AREA UNDER THE CURVE; ARTICLE; CEREBROSPINAL FLUID EXAMINATION; COMPARATIVE STUDY; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIGITAL IMAGING AND COMMUNICATIONS IN MEDICINE; DISEASE CLASSIFICATION; ENTORHINAL CORTEX; FALSE POSITIVE RESULT; FEMALE; HIPPOCAMPUS; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; JOINT REGRESSION; MAJOR CLINICAL STUDY; MALE; MATRIX SIMILARITY BASED LOSS FUNCTION; MILD COGNITIVE IMPAIRMENT; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PARAHIPPOCAMPAL GYRUS; PERIRHINAL CORTEX; POSITRON EMISSION TOMOGRAPHY; PREDICTIVE VALUE; PRIORITY JOURNAL; PROCESS OPTIMIZATION; RECEIVER OPERATING CHARACTERISTIC; REGRESSION ANALYSIS; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; STATISTICAL MODEL; UNCUS; VALIDATION PROCESS; BRAIN; CEREBROSPINAL FLUID; COMPUTER SIMULATION; MATHEMATICAL COMPUTING; PATHOLOGY; PROCEDURES; PROGNOSIS; SCINTISCANNING; VERY ELDERLY;

AGED; AGED, 80 AND OVER; ALZHEIMER DISEASE; BRAIN; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; FEMALE; HUMANS; MAGNETIC RESONANCE IMAGING; MALE; MATHEMATICAL COMPUTING; MILD COGNITIVE IMPAIRMENT; NEUROIMAGING; PROGNOSIS;

EID: 84903899707     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2014.05.078     Document Type: Article

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