Using deep learning to differentiate among histology renal tumor types in computed tomography scans.

Journal: BMC medical imaging
PMID:

Abstract

BACKGROUND: This study employed a convolutional neural network (CNN) to analyze computed tomography (CT) scans with the aim of differentiating among renal tumors according to histologic sub-type.

Authors

  • Hung-Cheng Kan
    In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, No.250, Wuxing St., Xinyi Dist., Taipei City, 110, Taiwan.
  • Po-Hung Lin
    Department of Medicine, Chang Gung University, Taoyuan City, Taiwan.
  • I-Hung Shao
    Department of Medicine, Chang Gung University, Taoyuan City, Taiwan.
  • Shih-Chun Cheng
    Taiwan AI Labs, Taipei, Taiwan.
  • Tzuo-Yau Fan
    Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, No. 5, Fuxing St., Guishan Dist., Taoyuan City, 333, Taiwan.
  • Ying-Hsu Chang
    Department of Medicine, Chang Gung University, Taoyuan City, Taiwan.
  • Liang-Kang Huang
    Department of Medicine, Chang Gung University, Taoyuan City, Taiwan.
  • Yuan-Cheng Chu
    Division of Urology, Department of Surgery, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan.
  • Kai-Jie Yu
    Department of Medicine, Chang Gung University, Taoyuan City, Taiwan.
  • Cheng-Keng Chuang
    Department of Medicine, Chang Gung University, Taoyuan City, Taiwan.
  • Chun-Te Wu
    Department of Medicine, Chang Gung University, Taoyuan City, Taiwan.
  • See-Tong Pang
    Department of Medicine, Chang Gung University, Taoyuan City, Taiwan.
  • Syu-Jyun Peng
    Biomedical Electronics Translational Research Center, National Chiao Tung University, Hsin-Chu, Taiwan; Institute of Electronics, National Chiao Tung University, Hsin-Chu, Taiwan. Electronic address: blue.year@msa.hinet.net.