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15 p, 294.1 KB The reliability of a deep learning model in clinical out-of-distribution MRI data : A multicohort study / Mårtensson, G. (Karolinska Institutet (Estocolm, Suècia)) ; Ferreira, D. (Karolinska Institutet (Estocolm, Suècia)) ; Granberg, T. (Karolinska University Hospital and Karolinska Institutet (Suecia)) ; Cavallin, L. (Karolinska University Hospital and Karolinska Institutet (Suecia)) ; Oppedal, K. (University of Stavanger) ; Padovani, A. (University of Brescia) ; Rektorova, I. (Masaryk University) ; Bonanni, Laura (University G d'Annunzio of Chieti-Pescara) ; Pardini, M. (University of Genoa and Neurology Clinics) ; Kramberger, M.G. (Univerza V Ljubljani) ; Taylor, J.P. (Newcastle University) ; Hort, J. (Charles University) ; Snædal, J. (Landspitali University Hospital (Reykjavík, Islàndia)) ; Kulisevsky, Jaime (Institut d'Investigació Biomèdica Sant Pau) ; Blanc, F. (University of Strasbourg and French National Centre for Scientific Research (CNRS)) ; Antonini, Angelo (University of Padua) ; Mecocci, Patrizia (University of Perugia) ; Vellas, B. (University of Toulouse) ; Tsolaki, Magda (3rd Aristotle University of Thessaloniki) ; Kłoszewska, Iwona (Medical University of Lodz) ; Soininen, H. (Kuopio University Hospital ( Finlàndia)) ; Lovestone, S. (University of Oxford) ; Simmons, A. (King's College London) ; Aarsland, Dag (King's College London) ; Westman, E. (King's College London) ; Universitat Autònoma de Barcelona
Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in medical imaging are often trained on public research cohorts with images acquired with a single scanner or with strict protocol harmonization, which is not representative of a clinical setting. [...]
2020 - 10.1016/j.media.2020.101714
Medical Image Analysis, Vol. 66 (december 2020) , p. 101714  
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