Inherent Structure-Based Multiview Learning with Multitemplate Feature Representation for Alzheimer's Disease Diagnosis

Mingxia Liu, Daoqiang Zhang, Ehsan Adeli, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

106 Citations (Scopus)


Multitemplate-based brain morphometric pattern analysis using magnetic resonance imaging has been recently proposed for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage (i.e., mild cognitive impairment or MCI). In such methods, multiview morphological patterns generated from multiple templates are used as feature representation for brain images. However, existing multitemplate-based methods often simply assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while in reality, the underlying data distribution is actually not preknown. In this paper, we propose an inherent structure-based multiview leaning method using multiple templates for AD/MCI classification. Specifically, we first extract multiview feature representations for subjects using multiple selected templates and then cluster subjects within a specific class into several subclasses (i.e., clusters) in each view space. Then, we encode those subclasses with unique codes by considering both their original class information and their own distribution information, followed by a multitask feature selection model. Finally, we learn an ensemble of view-specific support vector machine classifiers based on their, respectively, selected features in each view and fuse their results to draw the final decision. Experimental results on the Alzheimer's Disease Neuroimaging Initiative database demonstrate that our method achieves promising results for AD/MCI classification, compared to the state-of-the-art multitemplate-based methods.

Original languageEnglish
Article number7312947
Pages (from-to)1473-1482
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Issue number7
Publication statusPublished - 2016 Jul

Bibliographical note

Publisher Copyright:
© 2015 IEEE.


  • Alzheimer's disease (AD)
  • Multitask feature selection
  • disease diagnosis
  • multitemplate
  • multiview representation

ASJC Scopus subject areas

  • Biomedical Engineering


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