Deep convolutional neural network applied to the liver imaging reporting and data system (LI-RADS) version 2014 category classification: a pilot study.

Abstract

Authors

• Rikiya Yamashita
  Artera, Inc., Los Altos, CA.
• Amber Mittendorf
  Department of Radiology, Center for Advanced Magnetic Resonance Development, Duke University Medical Center, Durham, NC, USA.
• Zhe Zhu
• Kathryn J Fowler
  From the Department of Radiology, University of Wisconsin Madison School of Medicine and Public Health, 600 Highland Dr, Madison, WI 53792 (D.A.B., M.L.S.); Department of Radiology, New York University, New York, NY (L.M.); Department of Musculoskeletal Radiology (M.A.B.) and Institute for Technology Assessment (E.F.H.), Massachusetts General Hospital, Boston, Mass; Department of Medical Imaging, Hospital for Sick Children, University of Toronto, Toronto, Canada (B.B.E.W.); Department of Radiology, University of California-San Diego, San Diego, Calif (K.J.F.); Department of Cancer Imaging, Division of Imaging Sciences & Biomedical Engineering, Kings College London, London, England (V.J.G.); Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, Calif (C.P.H.); and Department of Radiology and Radiologic Science, The Johns Hopkins University School of Medicine, Baltimore, Md (C.R.W.).
• Cynthia S Santillan
  Liver Imaging Group, Department of Radiology, University of California San Diego, San Diego, CA, USA.
• Claude B Sirlin
  Liver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, CA, USA.
• Mustafa R Bashir
  Department of Radiology, Duke University, Durham, North Carolina, USA.
• Richard K G Do
  Department of Radiology, Body Imaging Service, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA. dok@mskcc.org.

Keywords

External Resources