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Neuroinformatics

, Volume 15, Issue 2, pp 115–132 | Cite as

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

  • Bo Cheng
  • Mingxia Liu
  • Dinggang ShenEmail author
  • Zuoyong Li
  • Daoqiang ZhangEmail author
  • the Alzheimer’s Disease Neuroimaging Initiative.
Original Article

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.

Keywords

Transfer learning Multi-domain Alzheimer’s disease (AD) Feature selection 

Notes

Acknowledgments

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: 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., as well as non-profit partners the Alzheimer’s Association and Alzheimer’s Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuron Imaging at the University of California, Los Angeles. This work was supported in part by the National Natural Science Foundation of China (Nos. 61602072, 61422204 and 61473149), the Chongqing Cutting-edge and Applied Foundation Research Program (Nos. cstc2016jcyjA0063, cstc2014jcyjA1316, and cstc2014jcyjA40035), the Scientific and Technological Research Program of Chongqing Municipal Education Commission (Nos. KJ1501014, KJ1401010, and KJ1601003), the NUAA Fundamental Research Funds (No. NE2013105), and NIH grants (AG041721, AG049371, AG042599, AG053867).

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Bo Cheng
    • 1
  • Mingxia Liu
    • 2
    • 3
  • Dinggang Shen
    • 3
    • 4
    Email author
  • Zuoyong Li
    • 5
  • Daoqiang Zhang
    • 2
    • 5
    Email author
  • the Alzheimer’s Disease Neuroimaging Initiative.
  1. 1.Key Laboratory of Advanced Network and Intellectual TechnologyChongqing Three Gorges UniversityChongqingChina
  2. 2.Department of Computer Science and EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  3. 3.Department of Radiology and BRICUniversity of North CarolinaChapel HillUSA
  4. 4.Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea
  5. 5.Fujian Provincial Key Laboratory of Information Processing and Intelligent ControlMinjiang UniversityFuzhouChina

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