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

    8 Citations (Scopus)

    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.

    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
    Pages446-454
    Number of pages9
    ISBN (Print)9783030009304
    DOIs
    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

    Other

    Other21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
    Country/TerritorySpain
    CityGranada
    Period18/9/1618/9/20

    Bibliographical note

    Publisher Copyright:
    © Springer Nature Switzerland AG 2018.

    Keywords

    • 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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