Multi-modal classification of Alzheimer's disease using nonlinear graph fusion

Pattern Recognition

Volumn 63, Issue , 2017, Pages 171-181

  Tong, Tong ^a,c   Gray, Katherine ^a   Gao, Qinquan ^b   Chen, Liang ^a   Rueckert, Daniel ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b FUZHOU UNIVERSITY   (China)

^c HARVARD MEDICAL SCHOOL   (United States)

Author keywords

Biomarkers; Classification of Alzheimer's disease; Machine learning; Multiple modalities; Nonlinear graph fusion

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOMARKERS; DIAGNOSIS; GENES; LEARNING SYSTEMS; MODAL ANALYSIS; NEURODEGENERATIVE DISEASES; NEUROIMAGING;

ALZHEIMER'S DISEASE; CLASSIFICATION FRAMEWORK; CLASSIFICATION METHODS; DIAGNOSIS AND PROGNOSIS; MILD COGNITIVE IMPAIRMENTS (MCI); MULTIPLE MODALITIES; RECEIVER OPERATOR CHARACTERISTICS; STATE-OF-THE-ART APPROACH;

COMPUTER AIDED DIAGNOSIS;

EID: 84999007060     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2016.10.009     Document Type: Article

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