Clinical insights to improve medical deep learning design: A comprehensive review of methods and benefits.

Journal: Computers in biology and medicine
Published Date:

Abstract

The success of deep learning and computer vision of natural images has led to an increased interest in medical image deep learning applications. However, introducing black-box deep learning models leaves little room for domain-specific knowledge when making the final diagnosis. For medical computer vision applications, not only accuracy, but also robustness, interpretability and explainability are essential to ensure trust for clinicians. Medical deep learning applications can therefore benefit from insights into the application at hand by involving clinical staff and considering the clinical diagnostic process. In this review, different clinically-inspired methods are surveyed, including clinical insights used at different stages of deep learning design for three-dimensional (3D) computed tomography (CT) image data. This review is conducted by investigating 400 research articles, covering different deep learning-based approaches for diagnosis of different diseases, in terms of including clinical insights in the published work. Based on this, a further detailed review is conducted of the 47 scientific articles using clinical inspiration. The clinically-inspired methods were found to be made with respect to preparation for training, 3D medical image data processing, integration of clinical data and model architecture selection and development. This highlights different ways in which domain-specific knowledge can be used in the design of deep learning systems.

Authors

  • Terese A E Thornblad
    Eindhoven University of Technology, De Groene Loper 19, Eindhoven, 5612 AP, The Netherlands. Electronic address: t.a.e.hellstrom@tue.nl.
  • Lotte J S Ewals
    Catharina hospital, Michelangelolaan 2, Eindhoven, 5623 EJ, The Netherlands.
  • Joost Nederend
    From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands (K.G.v.L., S.S., M.J.C.M.R., M.H., C.M.S.P., M.d.R., B.v.G., B.H.J.G., J.M.); Department of Radiology (M.J.C.M.R.) and Department of MICT and Imaging Techniques (T.S.), Jeroen Bosch Hospital, 's-Hertogenbosch, the Netherlands; Department of Radiology, Meander Medical Centre, Amersfoort, the Netherlands (C.M.S.P., M.V.); Department of Radiology, Hospital Gelderse Vallei, Ede, the Netherlands (B.M., M.M.V.); Department of Radiology, Noordwest Ziekenhuisgroep, Alkmaar, the Netherlands (C.F.v.D., P.A.); Department of Radiology & Nuclear Medicine, Máxima Medical Center, Eindhoven, the Netherlands (E.L.K., F.v.d.W.); Department of Radiology, Ziekenhuisgroep Twente, Almelo, the Netherlands (E.V.H., F.M.t.B., M.M., O.V., Y.H.G.v.B.F.); Center for Radiology and Nuclear Medicine, Deventer Hospital, Deventer, the Netherlands (E.L.V., J.M.L., M.N.); Department of Radiology, Catharina Hospital, Eindhoven, the Netherlands (E.M.J.M., J.N., K.M.E.M.); Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands (F.A.A.M.H.); Department of Radiology, Zaans Medisch Centrum, Zaandam, the Netherlands (F.v.H.); Department of Radiology and Nuclear Medicine, Amsterdam UMC-Location University of Amsterdam, Amsterdam, the Netherlands (I.A.H.v.d.B.); Department of Radiology & Nuclear Medicine, Haaglanden Medical Center, The Hague, the Netherlands (J.H.); Department of Radiology, Amsterdam University Medical Center, Amsterdam, the Netherlands (J.I.M.L.V.); Department of Radiology and Nuclear Medicine, Rijnstate, Arnhem, the Netherlands (L.N.D.); Department of Radiology, St Antonius Hospital, Nieuwegein, the Netherlands (L.C.M.L., S.A.); Department of Radiology, Isala Hospital, Zwolle, the Netherlands (M.F.B.); and Department of Radiology, Groene Hart Hospital, Gouda, the Netherlands (S.M.B.).
  • Misha D P Luyer
    Department of Surgery, Catharina Hospital Eindhoven, Eindhoven, Netherlands.
  • Peter H N de With
    Eindhoven University of Technology, 5612 AJ, Eindhoven, The Netherlands.
  • Fons van der Sommen
    VCA Research Group, Eindhoven University of Technology, Eindhoven, The Netherlands.

Keywords

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