Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI.

Journal: European radiology
PMID: 31016442

Abstract

OBJECTIVES: To develop and validate a proof-of-concept convolutional neural network (CNN)-based deep learning system (DLS) that classifies common hepatic lesions on multi-phasic MRI.

Authors

  • Charlie A Hamm
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA.
  • Clinton J Wang
    CSAIL/EECS, Massachusetts Institute of Technology, Cambridge, MA, 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.
  • James S Duncan
    Biomedical Engineering, Yale University, New Haven, CT 06511, USA.
  • Jeffrey C Weinreb
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT, 06520, 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.

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