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

The Alzheimer’S Disease Neuroimaging Initiative

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)115-132
Number of pages18
JournalNeuroinformatics
Volume15
Issue number2
DOIs
Publication statusPublished - 2017 Apr 1

Keywords

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

ASJC Scopus subject areas

  • Software
  • Neuroscience(all)
  • Information Systems

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