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

Bo Cheng, Mingxia Liu, Dinggang Shen, Zuoyong Li, Daoqiang Zhang, Initiative The Alzheimer’S Disease Neuroimaging

Research output: Contribution to journalArticle

17 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1-18
Number of pages18
Publication statusAccepted/In press - 2016 Dec 7


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

ASJC Scopus subject areas

  • Software
  • Neuroscience(all)
  • Information Systems

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  • Cite this

    Cheng, B., Liu, M., Shen, D., Li, Z., Zhang, D., & The Alzheimer’S Disease Neuroimaging, I. (Accepted/In press). Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer’s Disease. Neuroinformatics, 1-18.