IDA-MIL: Classification of Glomerular with Spike-like Projections via Multiple Instance Learning with Instance-level Data Augmentation.

Journal: Computer methods and programs in biomedicine
PMID: 36088891

Abstract

BACKGROUND AND OBJECTIVE: Tiny spike-like projections on the basement membrane of glomeruli are the main pathological feature of membranous nephropathy at stage II (MN II), which is the most significant stage for the diagnosis and treatment of renal disease. Pathological technology is the gold standard in the diagnosis of spike-like and other MNs, and automatic classification of spike-like projection is a crucial step in assisting pathologists in their diagnosis. However, owing to hard-to-label spile-like projections and the scarcity of patient data, classification of glomeruli with spike-like projections based on supervised learning methods is a challenging task.

Authors

  • Xi Wu
  • Yilin Chen
    College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China.
  • Xinyu Li
    School of Pharmacy, Binzhou Medical University, Yantai, China.
  • Xueyu Liu
    College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China.
  • Yifei Liu
    College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China.
  • Yongfei Wu
    College of Data Science, Taiyuan University of Technology, Taiyuan, 030024, China. Electronic address: wuyongfei@tyut.edu.cn.
  • Ming Li
    Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China.
  • Xiaoshuang Zhou
    Department of Nephrology, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China.
  • Chen Wang
    Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

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