CSEPC: a deep learning framework for classifying small-sample multimodal medical image data in Alzheimer's disease.

Journal: BMC geriatrics
PMID: 40011826

Abstract

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder that significantly impacts health care worldwide, particularly among the elderly population. The accurate classification of AD stages is essential for slowing disease progression and guiding effective interventions. However, limited sample sizes continue to present a significant challenge in classifying the stages of AD progression. Addressing this obstacle is crucial for improving diagnostic accuracy and optimizing treatment strategies for those affected by AD.

Authors

  • Jingyuan Liu
    Blood Testing Center, First Affiliated Hospital of Hunan University of Traditional Chinese Medicine, Changsha, 410007, Hunan, China.
  • Xiaojie Yu
    Language Computing Lab, Samsung R&D Institute of China - Beijing (SRC-B), Beijing, China.
  • Hidenao Fukuyama
    Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Toshiya Murai
    Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, 606-8501, Japan.
  • Jinglong Wu
  • Qi Li
    The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.
  • Zhilin Zhang
    State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.

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