Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network.

Journal: European radiology
Published Date: Feb 1, 2020

Abstract

OBJECTIVES: The aim of this study was to develop a deep convolutional neural network (DCNN) for the prediction of the METAVIR score using B-mode ultrasonography images.

Authors

  • Jeong Hyun Lee
    1 Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine , Seoul, Korea.
  • Ijin Joo
    Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
  • Tae Wook Kang
    Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-gu, Seoul, 06351, Republic of Korea. kaienes.kang@samsung.com.
  • Yong Han Paik
    Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
  • Dong Hyun Sinn
    Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
  • Sang Yun Ha
    Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
  • Kyunga Kim
    Statistics and Data Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea.
  • Choonghwan Choi
    Medical Imaging R&D Group, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seoul, Republic of Korea.
  • Gunwoo Lee
    Medical Imaging R&D Group, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seoul, Republic of Korea.
  • Jonghyon Yi
    Medical Imaging R&D Group, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seoul, Republic of Korea.
  • Won-Chul Bang

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