期刊
FRONTIERS IN AGING NEUROSCIENCE
卷 8, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fnagi.2016.00077
关键词
Alzheimer's disease diagnosis; bag of feature; canonical correlation analysis; fusion; normalization
资金
- National Natural Science Foundation of China [61402296, 61571304, 81571758, 61427806]
- (Key) Project of Department of Education of Guangdong Province [2014GKXM052]
- 48th Scientific Research Foundation for the Returned Overseas Chinese Scholars, Shenzhen Key Basic Research Project [JCYJ20150525092940986, JCYJ20130329105033277, JCYJ20140509172609164]
- Shenzhen-Hong Kong Innovation Circle Funding Program [JSE201109150013A]
To address the challenging task of diagnosing neurodegenerative brain disease, such as Alzheimer's disease (AD) and mild cognitive impairment (MCI), we propose a novel method using discriminative feature learning and canonical correlation analysis (CCA) in this paper. Specifically, multimodal features and their CCA projections are concatenated together to represent each subject, and hence both individual and shared information of AD disease are captured. A discriminative learning with multilayer feature hierarchy is designed to further improve performance. Also, hybrid representation is proposed to maximally explore data from multiple modalities. A novel normalization method is devised to tackle the infra- and inter-subject variations from the multimodal data. Based on our extensive experiments, our method achieves an accuracy of 96.93% [AD vs. normal control (NC)], 86.57 % (MCI vs. NC), and 82.75% [MCI converter (MCI-C) vs. MCI non-converter (MCI-NC)], respectively, which outperforms the state-of-the-art methods in the literature.
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