Multitemplate-based multiview learning for Alzheimer's disease diagnosis

M. Liu, R. Min, Y. Gao, D. Zhang, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

Abstract

Multitemplate-based brain morphometric pattern analysis using magnetic resonance imaging has recently been proposed for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage (ie, mild cognitive impairment or MCI). In such methods, multiview morphological patterns generated from multiple templates are used as feature representation for brain images. This chapter presents some of the latest advancements in multitemplate-based multiview learning for AD and MCI diagnosis. We will first present a multiview feature representation method by employing multiple templates. Then we will discuss how to make use of those multiview representations for effective diagnosis of AD and MCI. Specifically, we will introduce four multiview learning methods for AD/MCI classification, and demonstrate that these methods can further promote the disease diagnosis performance.

Original languageEnglish
Title of host publicationMachine Learning and Medical Imaging
PublisherElsevier Inc.
Pages259-297
Number of pages39
ISBN (Electronic)9780128041147
ISBN (Print)9780128040768
DOIs
Publication statusPublished - 2016 Aug 9
Externally publishedYes

    Fingerprint

Keywords

  • Alzheimer's disease
  • Classification
  • Disease diagnosis
  • Multitemplate
  • Multiview representation

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Liu, M., Min, R., Gao, Y., Zhang, D., & Shen, D. (2016). Multitemplate-based multiview learning for Alzheimer's disease diagnosis. In Machine Learning and Medical Imaging (pp. 259-297). Elsevier Inc.. https://doi.org/10.1016/B978-0-12-804076-8.00009-8