期刊
NEUROINFORMATICS
卷 15, 期 2, 页码 115-132出版社
HUMANA PRESS INC
DOI: 10.1007/s12021-016-9318-5
关键词
Transfer learning; Multi-domain; Alzheimer's disease (AD); Feature selection
资金
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- Abbott
- AstraZeneca AB
- Bayer Schering Pharma AG
- Bristol-Myers Squibb
- Eisai Global Clinical Development
- Elan Corporation
- Genentech
- GE Healthcare
- GlaxoSmithKline
- Innogenetics
- Johnson and Johnson
- Eli Lilly and Co.
- Medpace, Inc.
- Merck and Co., Inc.
- Novartis AG
- Pfizer Inc.
- F. Hoffman-La Roche
- Schering-Plough
- Synarc, Inc.
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- U. S. Food and Drug Administration
- Northern California Institute for Research and Education
- National Natural Science Foundation of China [61602072, 61422204, 61473149]
- Chongqing Cutting-edge and Applied Foundation [cstc2016jcyjA0063, cstc2014jcyjA1316, cstc2014jcyjA40035]
- Chongqing Municipal Education Commission [KJ1501014, KJ1401010, KJ1601003]
- NUAA Fundamental Research Funds [NE2013105]
- NIH [AG041721, AG049371, AG042599, AG053867]
Recently, transfer learning has been successfully applied in early diagnosis of Alzheimer's Disease (AD) based on multi-domain data. However, most of existing methods only use data from a single auxiliary domain, and thus cannot utilize the intrinsic useful correlation information from multiple domains. Accordingly, in this paper, we consider the joint learning of tasks in multi-auxiliary domains and the target domain, and propose a novel Multi-Domain Transfer Learning (MDTL) framework for early diagnosis of AD. Specifically, the proposed MDTL framework consists of two key components: 1) a multi-domain transfer feature selection (MDTFS) model that selects the most informative feature subset from multi-domain data, and 2) a multi-domain transfer classification (MDTC) model that can identify disease status for early AD detection. We evaluate our method on 807 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using baseline magnetic resonance imaging (MRI) data. The experimental results show that the proposed MDTL method can effectively utilize multi-auxiliary domain data for improving the learning performance in the target domain, compared with several state-of-the-art methods.
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