Temporal correlation structure learning for MCI conversion prediction

Xiaoqian Wang, Weidong Cai, Dinggang Shen, Heng Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
EditorsAlejandro F. Frangi, Christos Davatzikos, Gabor Fichtinger, Carlos Alberola-López, Julia A. Schnabel
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783030009304
Publication statusPublished - 2018
Event21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: 2018 Sept 162018 Sept 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11072 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2018.


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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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