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

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• 作者：Tong Tong^a ; ^c ; ^{ttraveltong@gmail.com}Author Vitae ; Katherine Gray^aAuthor Vitae ; Qinquan Gao^b ; ^{gqinquan@fzu.edu.cn}Author Vitae ; Liang Chen^aAuthor Vitae ; Daniel Rueckert^aAuthor Vitae ; The Alzheimer's Disease Neuroimaging Initiative¹
• 关键词：Multiple modalities ; Biomarkers ; Nonlinear graph fusion ; Machine learning ; Classification of Alzheimer's disease
• 刊名：Pattern Recognition
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：63
• 期：Complete
• 页码：171-181
• 全文大小：1326 K
• 卷排序：63

文摘

Multi-modality biomarkers were used for the classification of AD. Nonlinear graph fusion was used to investigate the multi-modal complementary information. Validations were performed in different classification scenarios. We achieved superior results than the state-of-the-art linear combination approaches. The proposed method provides an effective way to integrate multiple heterogeneous data for the classification of AD.