Multi-Modal Multi-Task Learning for Joint Prediction of Clinical Scores in Alzheimer’s Disease

  • Daoqiang Zhang
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7012)

Abstract

One recent interest in computer-aided diagnosis of neurological diseases is to predict the clinical scores from brain images. Most existing methods usually estimate multiple clinical variables separately, without considering the useful correlation information among them. On the other hand, nearly all methods use only one modality of data (mostly structural MRI) for regression, and thus ignore the complementary information among different modalities. To address these issues, in this paper, we present a general methodology, namely Multi-Modal Multi-Task (M3T) learning, to jointly predict multiple variables from multi-modal data. Our method contains three major subsequent steps: (1) a multi-task feature selection which selects the common subset of relevant features for the related multiple clinical variables from each modality; (2) a kernel-based multimodal data fusion which fuses the above-selected features from all modalities; (3) a support vector regression which predicts multiple clinical variables based on the previously learnt mixed kernel. Experimental results on ADNI dataset with both imaging modalities (MRI and PET) and biological modality (CSF) validate the efficacy of the proposed M3T learning method.

Keywords

Feature Selection Mild Cognitive Impairment Mini Mental State Examination Support Vector Regression Clinical Score 
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

  • Daoqiang Zhang
    • 1
    • 2
  • Dinggang Shen
    • 1
  1. 1.Dept. of Radiology and BRICUniversity of North Carolina at Chapel Hill
  2. 2.Dept. of Computer Science and EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina

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