Deep learning in digital pathology image analysis: a survey.

Journal: Frontiers of medicine
Published Date: Jul 29, 2020

Abstract

Deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasks. Traditional methods usually require hand-crafted domain-specific features, and DL methods can learn representations without manually designed features. In terms of feature extraction, DL approaches are less labor intensive compared with conventional machine learning methods. In this paper, we comprehensively summarize recent DL-based image analysis studies in histopathology, including different tasks (e.g., classification, semantic segmentation, detection, and instance segmentation) and various applications (e.g., stain normalization, cell/gland/region structure analysis). DL methods can provide consistent and accurate outcomes. DL is a promising tool to assist pathologists in clinical diagnosis.

Authors

  • Shujian Deng
    School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.
  • Xin Zhang
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
  • Wen Yan
    Department of Dermatology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
  • Eric I-Chao Chang
    Microsoft Research Asia, Beijing, China. eric.chang@microsoft.com.
  • Yubo Fan
    State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, China. yubofan@buaa.edu.cn.
  • Maode Lai
  • Yan Xu
    Department of Nephrology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China.

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