Integrating Convolutional Neural Networks and Multi-Task Dictionary Learning for Cognitive Decline Prediction with Longitudinal Images.

Journal: Journal of Alzheimer's disease : JAD
PMID: 32390615

Abstract

BACKGROUND: Disease progression prediction based on neuroimaging biomarkers is vital in Alzheimer's disease (AD) research. Convolutional neural networks (CNN) have been proved to be powerful for various computer vision research by refining reliable and high-level feature maps from image patches.

Authors

  • Qunxi Dong
    School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
  • Jie Zhang
    College of Physical Education and Health, Linyi University, Linyi, Shandong, China.
  • Qingyang Li
    School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
  • Junwen Wang
    Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, Scottsdale, Arizona, USAWang.Junwen@mayo.edu.
  • Natasha Leporé
    CIBORG Children's Hospital Los Angeles and University of Southern California Los Angeles CA USA.
  • Paul M Thompson
    Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Richard J Caselli
  • Jieping Ye
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Ml 48109.
  • Yalin Wang
    Comp.Sci.& Engin, Arizona State Univ, Arizona, USA.

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