Identification of benign and malignant pulmonary nodules on chest CT using improved 3D U-Net deep learning framework.

Journal: European journal of radiology
Published Date: May 23, 2020

Abstract

PURPOSE: To accurately distinguish benign from malignant pulmonary nodules with CT based on partial structures of 3D U-Net integrated with Capsule Networks (CapNets) and provide a reference for the early diagnosis of lung cancer.

Authors

  • Kaiqiang Yang
    Department of Radiology, Zhongshan Hospital, Dalian University, Dalian, Liaoning Province, China; Infervision, Beijing, China.
  • Jinsha Liu
    Department of Radiology, Zhongshan Hospital, Dalian University, Dalian, Liaoning Province, China.
  • Wen Tang
    Infervision, Beijing, China.
  • Huiling Zhang
    Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Center for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
  • Rongguo Zhang
    Infervision, Beijing, China.
  • Jun Gu
    School of Computer Science and Technology, Department of Telecommunications, Xi'an Jiaotong University, Xi'an, China.
  • Ruiping Zhu
    Department of Pathology, Zhongshan Hospital, Dalian University, Dalian, Liaoning Province, China.
  • Jingtong Xiong
    Second Hospital, Dalian Medical University, Dalian, Liaoning Province, China.
  • Xiaoshuang Ru
    Central Hospital, Dalian, Liaoning Province, China.
  • Jianlin Wu
    Department of Respiratory Medicine, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.

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