Automatic Localization and Identification of Thoracic Diseases from Chest X-rays with Deep Learning.

Journal: Current medical imaging
Published Date: Jan 1, 2022

Abstract

BACKGROUND: There are numerous difficulties in using deep learning to automatically locate and identify diseases in chest X-rays (CXR). The most prevailing two are the lack of labeled data of disease locations and poor model transferability between different datasets. This study aims to tackle these problems.

Authors

  • Shuai Zhang
    School of Information, Zhejiang University of Finance and Economics, Hangzhou, China.
  • Tianyi Tang
    School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, China.
  • Xin Peng
  • Yanqiu Zhang
    b Department of Otorhinolaryngology, Head and Neck Surgery , Xuzhou Cancer Hospital , Xuzhou City , Jiangsu Province , PR China.
  • Wen Yang
    Department of Pharmaceutical Analysis, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China; Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China.
  • Wenfei Li
    National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, China.
  • Xiaoyan Xin
    Department of Radiology, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China.
  • Jian Zhang
    College of Pharmacy, Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China.
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
  • Bing Zhang
    School of Information Science and Engineering, Yanshan University, Hebei Avenue, Qinhuangdao, 066004, China.

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