Deep learning pneumoconiosis staging and diagnosis system based on multi-stage joint approach.

Journal: BMC medical imaging
PMID: 38956579

Abstract

BACKGROUND: Pneumoconiosis has a significant impact on the quality of patient survival due to its difficult staging diagnosis and poor prognosis. This study aimed to develop a computer-aided diagnostic system for the screening and staging of pneumoconiosis based on a multi-stage joint deep learning approach using X-ray chest radiographs of pneumoconiosis patients.

Authors

  • Chang Liu
    Key Lab of Cell Differentiation and Apoptosis of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Yeqi Fang
    College of Physics, Sichuan University, Chengdu, 610041, PR China.
  • YuHuan Xie
    West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610041, PR China.
  • Hao Zheng
    Gilead Sciences, Inc, Foster City, California, USA.
  • Xin Li
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Dongsheng Wu
  • Tao Zhang
    Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, 40044, People's Republic of China.

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