Maximum-margin based representation learning from multiple atlases for Alzheimer's disease classification

Rui Min, Jian Cheng, True Price, Guorong Wu, Dinggang Shen

Research output: Contribution to journalArticle

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)212-219
Number of pages8
JournalMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Volume17
Publication statusPublished - 2014

ASJC Scopus subject areas

  • Medicine(all)

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