Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Guffens, Frederic"'
Autor:
Fidon, Lucas, Aertsen, Michael, Kofler, Florian, Bink, Andrea, David, Anna L., Deprest, Thomas, Emam, Doaa, Guffens, Frédéric, Jakab, András, Kasprian, Gregor, Kienast, Patric, Melbourne, Andrew, Menze, Bjoern, Mufti, Nada, Pogledic, Ivana, Prayer, Daniela, Stuempflen, Marlene, Van Elslander, Esther, Ourselin, Sébastien, Deprest, Jan, Vercauteren, Tom
Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermin
Externí odkaz:
http://arxiv.org/abs/2204.02779
Autor:
Fidon, Lucas, Aertsen, Michael, Mufti, Nada, Deprest, Thomas, Emam, Doaa, Guffens, Frédéric, Schwartz, Ernst, Ebner, Michael, Prayer, Daniela, Kasprian, Gregor, David, Anna L., Melbourne, Andrew, Ourselin, Sébastien, Deprest, Jan, Langs, Georg, Vercauteren, Tom
The performance of deep neural networks typically increases with the number of training images. However, not all images have the same importance towards improved performance and robustness. In fetal brain MRI, abnormalities exacerbate the variability
Externí odkaz:
http://arxiv.org/abs/2108.04175
Autor:
Fidon, Lucas, Aertsen, Michael, Emam, Doaa, Mufti, Nada, Guffens, Frédéric, Deprest, Thomas, Demaerel, Philippe, David, Anna L., Melbourne, Andrew, Ourselin, Sébastien, Deprest, Jan, Vercauteren, Tom
Deep neural networks have increased the accuracy of automatic segmentation, however, their accuracy depends on the availability of a large number of fully segmented images. Methods to train deep neural networks using images for which some, but not al
Externí odkaz:
http://arxiv.org/abs/2107.03846
Autor:
Fidon, Lucas, Aertsen, Michael, Deprest, Thomas, Emam, Doaa, Guffens, Frédéric, Mufti, Nada, Van Elslander, Esther, Schwartz, Ernst, Ebner, Michael, Prayer, Daniela, Kasprian, Gregor, David, Anna L., Melbourne, Andrew, Ourselin, Sébastien, Deprest, Jan, Langs, Georg, Vercauteren, Tom
Limiting failures of machine learning systems is of paramount importance for safety-critical applications. In order to improve the robustness of machine learning systems, Distributionally Robust Optimization (DRO) has been proposed as a generalizatio
Externí odkaz:
http://arxiv.org/abs/2001.02658
Akademický článek
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Autor:
Topff, Laurens, Groot Lipman, Kevin B.W., Guffens, Frederic, Wittenberg, Rianne, Bartels-Rutten, Annemarieke, van Veenendaal, Gerben, Hess, Mirco, Lamerigts, Kay, Wakkie, Joris, Ranschaert, Erik, Trebeschi, Stefano, Visser, Jacob J., Beets-Tan, Regina G.H.
Publikováno v:
European Radiology, 33(6), 4249-4258. Springer-Verlag
European Radiology. Springer, Cham
European Radiology. Springer, Cham
Objectives Only few published artificial intelligence (AI) studies for COVID-19 imaging have been externally validated. Assessing the generalizability of developed models is essential, especially when considering clinical implementation. We report th
Akademický článek
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