Interpreting deep learning models for glioma survival classification using visualization and textual explanations.

Journal: BMC medical informatics and decision making
Published Date: Oct 18, 2023

Abstract

BACKGROUND: Saliency-based algorithms are able to explain the relationship between input image pixels and deep-learning model predictions. However, it may be difficult to assess the clinical value of the most important image features and the model predictions derived from the raw saliency map. This study proposes to enhance the interpretability of saliency-based deep learning model for survival classification of patients with gliomas, by extracting domain knowledge-based information from the raw saliency maps.

Authors

  • Michael Osadebey
    Department of Computer Science, Norwegian University of Science and Technology, Gjøvik, Norway. michael.osadebey@ntnu.no.
  • Qinghui Liu
    Department of Physics and Computational Radiology, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Sognsvannsveien 20, 0372, Oslo, Norway.
  • Elies Fuster-Garcia
    Grupo de Informática Biomédica (IBIME), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain.
  • Kyrre E Emblem
    From the Intervention Centre (K.E.E., A.B.), Department of Radiology (P.D.T., J.K.H.), and Department of Neurosurgery (T.R.M.), Oslo University Hospital, N-0027 Sognsvannsveien 20, 0372 Oslo, Norway; Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (K.E.E., M.C.P., O.R.); Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (M.C.P.); Department of Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany (F.G.Z., L.R.S.); and Department of Physics, University of Oslo, Oslo, Norway (A.B.).

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