One deep learning local-global model based on CT imaging to differentiate between nodular cryptococcosis and lung cancer which are hard to be diagnosed.

Journal: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Published Date: Dec 1, 2021

Abstract

OBJECTIVES: We aim to evaluate a deep learning (DL) model and radiomic model for preoperative differentiation of nodular cryptococcosis from solitary lung cancer in patients with malignant features on CT images.

Authors

  • Sheng Li
    School of Data Science, University of Virginia, Charlottesville, VA, United States.
  • Guizhi Zhang
    Department of Radiology, The Eighth Hospital of Sun Yat-sen University, China. Electronic address: 42484877@qq.com.
  • Youbing Yin
    Department of Engineering, CuraCloud Corporation, Seattle, WA, USA.
  • Qiuxia Xie
    Department of Radiology, The Eighth Hospital of Sun Yat-sen University, China. Electronic address: xqx@qq.com.
  • Xinyu Guo
    Keya Medical Co., Ltd, Longong, Shenzhen, China. Electronic address: xinyug@keyamedical.com.
  • Kunlin Cao
    Department of Engineering, CuraCloud Corporation, Seattle, WA, USA.
  • Qi Song
    ‡ College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
  • Jian Guan
    State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China; College of Chemical Engineering, University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Science and Technology on Particle Materials, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 361021, China.
  • Xuhui Zhou
    Department of Radiology, The Eighth Hospital of Sun Yat-sen University, China. Electronic address: zhouxuh@mail.sysu.edu.cn.

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