Evaluation of a deep learning-based computer-aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images.

Journal: Medical physics
Published Date: Jun 25, 2020

Abstract

PURPOSE: Computer-aided diagnosis (CAD) systems assist in solving subjective diagnosis problems that typically rely on personal experience. A CAD system has been developed to differentiate malignant thyroid nodules from benign thyroid nodules in ultrasound images based on deep learning methods. The diagnostic performance was compared between the CAD system and the experienced attending radiologists.

Authors

  • Chao Sun
    Hospital for Skin Diseases and Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China.
  • Yukang Zhang
    Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China.
  • Qing Chang
    Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
  • Tianjiao Liu
    State Key Laboratory of Intelligent Technology and Systems, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. Electronic address: ltj14@mails.tsinghua.edu.cn.
  • Shaohang Zhang
    Department of Ultrasound, Beijing Haidian Hospital, Haidian Section of Peking University Third Hospital, Beijing, 100080, China.
  • Xi Wang
    School of Information, Central University of Finance and Economics, Beijing, China.
  • Qianqian Guo
    National Cancer Center/Cancer Hospital of Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
  • Jinpeng Yao
    Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
  • Weidong Sun
    State Key Laboratory of Intelligent Technology and Systems, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China. Electronic address: wdsun@tsinghua.edu.cn.
  • Lijuan Niu
    National Cancer Center/Cancer Hospital of Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China. Electronic address: niulijuan8197@126.com.

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