Application of cloud server-based machine learning for assisting pathological structure recognition in IgA nephropathy.

Journal: Journal of clinical pathology
PMID: 38123970

Abstract

BACKGROUND: Machine learning (ML) models can help assisting diagnosis by rapidly localising and classifying regions of interest (ROIs) within whole slide images (WSIs). Effective ML models for clinical decision support require a substantial dataset of 'real' data, and in reality, it should be robust, user-friendly and universally applicable.

Authors

  • Yu-Lin Huang
    Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
  • Xiao Qi Liu
    Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
  • Yang Huang
    School of Computer and Electronic Information, Nanjing Normal University, Nanjing 210023, China.
  • Feng Yong Jin
    Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
  • Qing Zhao
    Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
  • Qin Yi Wu
    Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
  • Kun Ling Ma
    Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.

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