A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer's disease classification.

Journal: Magnetic resonance imaging
Published Date: Feb 13, 2021

Abstract

PURPOSE: Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. In recent years, machine learning methods have been widely used on analysis of neuroimage for quantitative evaluation and computer-aided diagnosis of AD or prediction on the conversion from mild cognitive impairment (MCI) to AD. In this study, we aimed to develop a new deep learning method to detect or predict AD in an efficient way.

Authors

  • Jie Zhang
    College of Physical Education and Health, Linyi University, Linyi, Shandong, China.
  • Bowen Zheng
    Department of Mechanical Engineering, University of California, Berkeley, CA, 94720, USA.
  • Ang Gao
    Reasearch Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China.
  • Xin Feng
    State Key Laboratory for Zoonotic Diseases, College of Veterinary Medicine, Jilin University, Changchun, China.
  • Dong Liang
    Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China.
  • Xiaojing Long
    Reasearch Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China; Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, China. Electronic address: xj.long@siat.ac.cn.

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