Glomerular disease classification and lesion identification by machine learning.

Journal: Biomedical journal
Published Date: Sep 8, 2021

Abstract

BACKGROUND: Classification of glomerular diseases and identification of glomerular lesions require careful morphological examination by experienced nephropathologists, which is labor-intensive, time-consuming, and prone to interobserver variability. In this regard, recent advance in machine learning-based image analysis is promising.

Authors

  • Cheng-Kun Yang
  • Ching-Yi Lee
    aetherAI, Co., Ltd., Taipei, Taiwan.
  • Hsiang-Sheng Wang
    Department of Anatomic Pathology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
  • Shun-Chen Huang
    Department of Anatomic Pathology, Chang Gung Memorial Hospital at Kaohsiung, Kaohsiung, Taiwan.
  • Peir-In Liang
    Department of Pathology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
  • Jung-Sheng Chen
    Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
  • Chang-Fu Kuo
    Department of Rheumatology, Allergy, and Immunology, Chang Gung Memorial Hospital, Taipei, Taiwan, ROC.
  • Kun-Hua Tu
    Department of Nephrology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
  • Chao-Yuan Yeh
  • Tai-Di Chen
    Department of Anatomic Pathology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan. Electronic address: b8902028@msn.com.

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