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Temporal Correlation Structure Learning for MCI Conversion Prediction

  • Xiaoqian Wang
  • Weidong Cai
  • Dinggang Shen
  • Heng HuangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11072)

Abstract

In Alzheimer’s research, Mild Cognitive Impairment (MCI) is an important intermediate stage between normal aging and Alzheimer’s. How to distinguish MCI samples that finally convert to AD from those do not is an essential problem in the prevention and diagnosis of Alzheimer’s. Traditional methods use various classification models to distinguish MCI converters from non-converters, while the performance is usually limited by the small number of available data. Moreover, previous methods only use the data at baseline time for training but ignore the longitudinal information at other time points along the disease progression. To tackle with these problems, we propose a novel deep learning framework that uncovers the temporal correlation structure between adjacent time points in the disease progression. We also construct a generative framework to learn the inherent data distribution so as to produce more reliable data to strengthen the training process. Extensive experiments on the ADNI cohort validate the superiority of our model.

Keywords

Deep learning Temporal correlation structure MCI conversion prediction Alzheimer’s disease 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xiaoqian Wang
    • 1
  • Weidong Cai
    • 2
  • Dinggang Shen
    • 3
  • Heng Huang
    • 1
    Email author
  1. 1.Department of Electrical and Computer EngineeringUniversity of PittsburghPittsburghUSA
  2. 2.School of Information TechnologiesUniversity of SydneySydneyAustralia
  3. 3.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA

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