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Predicting Clinical Scores Using Semi-supervised Multimodal Relevance Vector Regression

  • Bo Cheng
  • Daoqiang Zhang
  • Songcan Chen
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)

Abstract

We present a novel semi-supervised multimodal relevance vector regression (SM-RVR) method for predicting clinical scores of neurological diseases from multimodal brain images, to help evaluate pathological stage and predict future progression of diseases, e.g., Alzheimer’s diseases (AD). Different from most existing methods, we predict clinical scores from multimodal (imaging and biological) biomarkers, including MRI, FDG-PET, and CSF. Also, since mild cognitive impairment (MCI) subjects generally contain more noises in their clinical scores compared to AD and healthy control (HC) subjects, we use only their multimodal data (i.e., MRI, FDG-PET and CSF), not their clinical scores, to train a semi-supervised model for enhancing the estimation of clinical scores for AD and healthy control (HC). Experimental results on ADNI dataset validate the efficacy of the proposed method.

Keywords

Mild Cognitive Impairment Mini Mental State Examination Clinical Score Regression Performance Unlabeled Sample 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Bo Cheng
    • 1
  • Daoqiang Zhang
    • 1
    • 2
  • Songcan Chen
    • 1
  • Dinggang Shen
    • 2
  1. 1.Dept. of Computer Science and EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Dept. of Radiology and BRICUniversity of North CarolinaChapel Hill

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