Unveiling pathology-related predictive uncertainty of glomerular lesion recognition using prototype learning.

Journal: Journal of biomedical informatics
PMID: 39746430

Abstract

OBJECTIVE: Recognizing glomerular lesions is essential in diagnosing chronic kidney disease. However, deep learning faces challenges due to the lesion heterogeneity, superposition, progression, and tissue incompleteness, leading to uncertainty in model predictions. Therefore, it is crucial to analyze pathology-related predictive uncertainty in glomerular lesion recognition and unveil its relationship with pathological properties and its impact on model performance.

Authors

  • Qiming He
    Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China.
  • Yingming Xu
    Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, China.
  • Qiang Huang
    Department of Orthopedics, West China Hospital, Sichuan University, Chengdu Sichuan, 610041, P.R.China.
  • Yanxia Wang
    State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
  • Jing Ye
    d Department of Digestive System Diseases, The First Affiliated Hospital, Shihezi University School of Medicine, Shihezi, Xinjiang Province, China.
  • Yonghong He
    Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China.
  • Jing Li
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Lianghui Zhu
    Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China.
  • Zhe Wang
    Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China.
  • Tian Guan
    Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science, South China Agricultural University, Guangzhou, 510642, China.

Keywords

External Resources

Popular Topics
Recent Journals