Detection of liver cirrhosis in standard T2-weighted MRI using deep transfer learning.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: To investigate the diagnostic performance of deep transfer learning (DTL) to detect liver cirrhosis from clinical MRI.

Authors

  • Sebastian Nowak
    From the Quantitative Imaging Lab, Department of Radiology.
  • Narine Mesropyan
    Department of Diagnostic and Interventional Radiology, Quantitative Imaging Lab Bonn (QILaB), University Hospital Bonn (Universitätsklinikum Bonn), Venusberg-Campus 1, 53127, Bonn, Germany.
  • Anton Faron
    Department of Radiology, University Hospital Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Germany. Anton.Faron@ukbonn.de.
  • Wolfgang Block
    From the Quantitative Imaging Lab, Department of Radiology.
  • Martin Reuter
    Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; German Centre for Neurodegenerative Diseases (DZNE), Department of Image Analysis, Bonn, Germany.
  • Ulrike I Attenberger
    Department of Diagnostic and Interventional Radiology, Quantitative Imaging Lab Bonn (QILaB), University Hospital Bonn (Universitätsklinikum Bonn), Venusberg-Campus 1, 53127, Bonn, Germany.
  • Julian A Luetkens
    Department of Radiology, University Hospital Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Germany.
  • Alois M Sprinkart
    From the Quantitative Imaging Lab, Department of Radiology.