DML-MFCM: A multimodal fine-grained classification model based on deep metric learning for Alzheimer's disease diagnosis.

Journal: Journal of X-ray science and technology
PMID: 39973767

Abstract

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder. There are no drugs and methods for the treatment of AD, but early intervention can delay the deterioration of the disease. Therefore, the early diagnosis of AD and mild cognitive impairment (MCI) is significant. Structural magnetic resonance imaging (sMRI) is widely used to present structural changes in the subject's brain tissue. The relatively mild structural changes in the brain with MCI have led to ongoing challenges in the task of conversion prediction in MCI. Moreover, many multimodal AD diagnostic models proposed in recent years ignore the potential relationship between multimodal information.

Authors

  • Heng Wang
    Fujian Key Laboratory of Traditional Chinese Veterinary Medicine and Animal Health, College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou, China.
  • Tiejun Yang
    Key Laboratory of Grain Information Processing and Control (HAUT), Ministry of Education, Zhengzhou, China.
  • JiaCheng Fan
    School of Mechanical Engineering, Shanghai Jiao Tong University, Room 901, Dongchuan Road 800, Minhang District, Shanghai, 200240, China.
  • Huiyao Zhang
    School of Information Science and Engineering, Henan University of Technology, Zhengzhou, China.
  • Wenjie Zhang
    Laboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital of Sichuan University, No. 37 Guo Xue Alley, Chengdu, 610041, People's Republic of China.
  • Mingzhu Ji
    School of Information Science and Engineering, Henan University of Technology, Zhengzhou, China.
  • Jianyu Miao
    College of Information Science and Engineering, Henan University of Technology, Zhengzhou, 450001, China. Electronic address: jymiao@haut.edu.cn.

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