Size-adaptive mediastinal multilesion detection in chest CT images via deep learning and a benchmark dataset.

Journal: Medical physics
PMID: 35689486

Abstract

PURPOSE: Many deep learning methods have been developed for pulmonary lesion detection in chest computed tomography (CT) images. However, these methods generally target one particular lesion type, that is, pulmonary nodules. In this work, we intend to develop and evaluate a novel deep learning method for a more challenging task, detecting various benign and malignant mediastinal lesions with wide variations in sizes, shapes, intensities, and locations in chest CT images.

Authors

  • Jun Wang
    Department of Speech, Language, and Hearing Sciences and the Department of Neurology, The University of Texas at Austin, Austin, TX 78712, USA.
  • Xiawei Ji
    College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
  • Mengmeng Zhao
    MOE Key Laboratory of Analysis and Detection for Food Safety, Fujian Provincial Key Laboratory of Analysis and Detection Technology for Food Safety, College of Chemistry, Fuzhou University, Fuzhou, Fujian 350108, China.
  • Yaofeng Wen
  • Yunlang She
    Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
  • Jiajun Deng
    Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
  • Chang Chen
    Biomass Energy and Environmental Engineering Research Center, College of Chemical Engineering, Beijing University of Chemical Technology, 505 Zonghe Building A, 15 North 3rd Ring East Road, Beijing, 100029, China. chenchang@mail.buct.edu.cn.
  • Dahong Qian
  • Hongbing Lu
    The Fourth Medical University, Department of of Biomedical Engineering, Xi'an, China.
  • Deping Zhao
    Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.

Keywords

External Resources

Popular Topics
Recent Journals