Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features.

Abstract

Authors

• Clinton J Wang
  CSAIL/EECS, Massachusetts Institute of Technology, Cambridge, MA, USA.
• Charlie A Hamm
  Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA.
• Lynn J Savic
  Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA.
• Marc Ferrante
  Department of Chronic Diseases and Metabolism (CHROMETA), Translational Research Center for Gastrointestinal Disorders (TARGID), KU Leuven, Herestraat 49, 3000 Leuven, Belgium.
• Isabel Schobert
  Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA.
• Todd Schlachter
  Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06520.
• MingDe Lin
  Philips Research North America, Cambridge, Massachusetts.
• Jeffrey C Weinreb
  Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA.
• James S Duncan
  Biomedical Engineering, Yale University, New Haven, CT 06511, USA.
• Julius Chapiro
  Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06520. Electronic address: julius.chapiro@yale.edu.
• Brian Letzen
  Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, The Anlyan Center, N312A, New Haven, CT 06520.

Keywords

External Resources