Automatic deep learning-based colorectal adenoma detection system and its similarities with pathologists.

Journal: BMJ open
Published Date: Sep 10, 2020

Abstract

OBJECTIVES: The microscopic evaluation of slides has been gradually moving towards all digital in recent years, leading to the possibility for computer-aided diagnosis. It is worthwhile to know the similarities between deep learning models and pathologists before we put them into practical scenarios. The simple criteria of colorectal adenoma diagnosis make it to be a perfect testbed for this study.

Authors

  • Zhigang Song
    Department of Pathology, The Chinese PLA General Hospital, 100853, Beijing, China.
  • Chunkai Yu
    Department of Pathology, Beijing Shijitan Hospital, Capital Medical University, 100038, Beijing, China.
  • Shuangmei Zou
    Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China.
  • Wenmiao Wang
    Department of Pathology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Yong Huang
    State Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources, Key Laboratory of Ecology of Rare and Endangered Species and Environmental Protection of Ministry Education, Guangxi Normal University, Guilin 541004, China.
  • Xiaohui Ding
    Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
  • Jinhong Liu
    Department of Pathology, The Chinese PLA General Hospital, 100853, Beijing, China.
  • Liwei Shao
    Department of Pathology, The Chinese PLA General Hospital, 100853, Beijing, China.
  • Jing Yuan
    School of Acu-Mox and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
  • Xiangnan Gou
    Department of Pathology, The Chinese PLA General Hospital, 100853, Beijing, China.
  • Wei Jin
    Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, PR China; Institute of Cardiovascular Diseases, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, PR China. Electronic address: jinwei1125@126.com.
  • Zhanbo Wang
    Department of Pathology, The Chinese PLA General Hospital, 100853, Beijing, China.
  • Xin Chen
    University of Nottingham, Nottingham, United Kingdom.
  • Huang Chen
    School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China.
  • Cancheng Liu
    Thorough Images, 100102, Beijing, China.
  • Gang Xu
    University Hospitals of Leicester NHS Trust; UK.
  • Zhuo Sun
    State Key Laboratory of Eye Health, Eye Hospital and Institute for Advanced Study on Eye Health and Diseases, Wenzhou Medical University, Wenzhou, China; Department of Ophthalmology, The Third People's Hospital of Changzhou, Changzhou, China.
  • Calvin Ku
    Thorough Images, 100102, Beijing, China.
  • Yongqiang Zhang
    School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, China.
  • Xianghui Dong
    Department of Pathology, Chinese PLA General Hospital, Beijing, China.
  • Shuhao Wang
    Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, P. R. China.
  • Wei Xu
    College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023 China.
  • Ning Lv
    Department of Pathology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Huaiyin Shi
    Department of Pathology, The Chinese PLA General Hospital, 100853, Beijing, China. shihuaiyin@sina.com.

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