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Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer's Disease

Authors
Cheng, BoLiu, MingxiaShen, DinggangLi, ZuoyongZhang, Daoqiang
Issue Date
4월-2017
Publisher
HUMANA PRESS INC
Keywords
Transfer learning; Multi-domain; Alzheimer' s disease (AD); Feature selection
Citation
NEUROINFORMATICS, v.15, no.2, pp.115 - 132
Indexed
SCIE
SCOPUS
Journal Title
NEUROINFORMATICS
Volume
15
Number
2
Start Page
115
End Page
132
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/84030
DOI
10.1007/s12021-016-9318-5
ISSN
1539-2791
Abstract
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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