Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis

Brain Structure and Function

Volumn 221, Issue 5, 2016, Pages 2569-2587

  Suk, Heung Il ^a   Lee, Seong Whan ^a   Shen, Dinggang ^a,b  

^a Korea University   (South Korea)

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

Author keywords

Alzheimer s disease (AD); Deep architecture; Feature selection; Magnetic resonance imaging (MRI); Mild cognitive impairment (MCI); Multi task learning; Positron emission topography (PET); Sparse least squared regression

Indexed keywords

AMYLOID BETA PROTEIN[1-42]; BIOLOGICAL MARKER; TAU PROTEIN;

ADULT; AGED; ALZHEIMER DISEASE; ANTERIOR COMMISSURE; ARTICLE; CEREBELLUM; CEREBROSPINAL FLUID ANALYSIS; CLUSTER ANALYSIS; COHORT ANALYSIS; DEEP SPARSE MULTITASK LEARNING; DIAGNOSTIC TEST ACCURACY STUDY; FEMALE; GRAY MATTER; HUMAN; LEARNING TEST; MAJOR CLINICAL STUDY; MALE; MILD COGNITIVE IMPAIRMENT; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; POSITRON EMISSION TOMOGRAPHY; POSTERIOR COMMISSURE; PREDICTIVE VALUE; PRIORITY JOURNAL; REGRESSION ANALYSIS; RESPONSE VARIABLE; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; WHITE MATTER; ALGORITHM; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; METABOLISM; PATHOLOGY; PROCEDURES;

AGED; ALGORITHMS; ALZHEIMER DISEASE; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; MAGNETIC RESONANCE IMAGING; MALE; POSITRON-EMISSION TOMOGRAPHY; REGRESSION ANALYSIS; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE;

EID: 84929692240     PISSN: 18632653     EISSN: 18632661     Source Type: Journal    
DOI: 10.1007/s00429-015-1059-y     Document Type: Article

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