Radiomics analysis combining unsupervised learning and handcrafted features: A multiple-disease study.

Journal: Medical physics
Published Date: Sep 10, 2021

Abstract

PURPOSE: To study and investigate the synergistic benefit of incorporating both conventional handcrafted and learning-based features in disease identification across a wide range of clinical setups.

Authors

  • Yidong Wan
    Institute of Translational Medicine, Zhejiang University, Hangzhou, China.
  • Pengfei Yang
    Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Lei Xu
    Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
  • Jing Yang
    Beijing Novartis Pharma Co. Ltd., Beijing, China.
  • Chen Luo
    Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Feng Chen
    Department of Integrated Care Management Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Yan Wu
    Beijing Hui-Long-Guan Hospital, Peking University, Beijing, 100096, China.
  • Yun Lu
    Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China.
  • Dan Ruan
    Departments of Radiation Oncology, Biomedical Physics and Bioengineering, UCLA, Los Angeles, CA, 90095, USA.
  • Tianye Niu
    Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.

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