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Maximum-Margin Based Representation Learning from Multiple Atlases for Alzheimer’s Disease Classification

  • Rui Min
  • Jian Cheng
  • True Price
  • Guorong Wu
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

In order to establish the correspondences between different brains for comparison, spatial normalization based morphometric measurements have been widely used in the analysis of Alzheimer’s disease (AD). In the literature, different subjects are often compared in one atlas space, which may be insufficient in revealing complex brain changes. In this paper, instead of deploying one atlas for feature extraction and classification, we propose a maximum-margin based representation learning (MMRL) method to learn the optimal representation from multiple atlases. Unlike traditional methods that perform the representation learning separately from the classification, we propose to learn the new representation jointly with the classification model, which is more powerful in discriminating AD patients from normal controls (NC). We evaluated the proposed method on the ADNI database, and achieved 90.69% for AD/NC classification and 73.69% for p-MCI/s-MCI classification.

Keywords

Support Vector Machine Feature Selection Optimal Representation Registration Error Multiple Kernel Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Rui Min
    • 1
  • Jian Cheng
    • 1
  • True Price
    • 1
  • Guorong Wu
    • 1
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and Biomedical Research Imaging Center (BRIC)University of North CarolinaChapel HillUSA

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