Robust multi-modal fusion architecture for medical data with knowledge distillation.

Journal: Computer methods and programs in biomedicine
PMID: 39709743

Abstract

BACKGROUND: The fusion of multi-modal data has been shown to significantly enhance the performance of deep learning models, particularly on medical data. However, missing modalities are common in medical data due to patient specificity, which poses a substantial challenge to the application of these models.

Authors

  • Muyu Wang
    School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China. Electronic address: whwangmy@ccmu.edu.cn.
  • Shiyu Fan
    Department of Radiology, The First People's Hospital of Kashi (Kashgar) Prefecture, China.
  • Yichen Li
    School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China.
  • Binyu Gao
    Biological Science & Medical Engineering, Southeast University, Nanjing, 518000, China.
  • Zhongrang Xie
    School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China.
  • Hui Chen
    Xiangyang Central HospitalAffiliated Hospital of Hubei University of Arts and Science Xiangyang 441000 China.

Keywords

External Resources

Popular Topics
Recent Journals