@article{945196,
      note = {"Organized as a satellite event of the 23rd International  Conference on Medical Image Computing and Computer Assisted  Intervention (MICCAI 2020) in Lima, Peru, which was held  completely virtually due to the COVID-19  pandemic."--Preface},
      author = {Sudre, Carole H., and Fehri, Hamid., and Arbel, Tal, and  Baumgartner, Christian F., and Dalca, Adrian., and Tanno,  Ryutaro., and Van Leemput, Koen., and Wells, William M.,  and Sotiras, Aristeidis., and Papiez, Bartlomiej., and  Ferrante, Enzo., and Parisot, Sarah.,},
      url = {http://library.usi.edu/record/945196},
      title = {Uncertainty for safe utilization of machine learning in  medical imaging, and graphs in biomedical image analysis :  second International Workshop, UNSURE 2020, and Third  International Workshop, GRAIL 2020, held in conjunction  with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings  /. UNSURE (Workshop)},
      abstract = {This book constitutes the refereed proceedings of the  Second International Workshop on Uncertainty for Safe  Utilization of Machine Learning in Medical Imaging, UNSURE  2020, and the Third International Workshop on Graphs in  Biomedical Image Analysis, GRAIL 2020, held in conjunction  with MICCAI 2020, in Lima, Peru, in October 2020. The  workshops were held virtually due to the COVID-19 pandemic.  For UNSURE 2020, 10 papers from 18 submissions were  accepted for publication. They focus on developing  awareness and encouraging research in the field of  uncertainty modelling to enable safe implementation of  machine learning tools in the clinical world. GRAIL 2020  accepted 10 papers from the 12 submissions received. The  workshop aims to bring together scientists that use and  develop graph-based models for the analysis of biomedical  images and to encourage the exploration of graph-based  models for difficult clinical problems within a variety of  biomedical imaging contexts.},
      recid = {945196},
      pages = {1 online resource (XVII, 222 pages) :},
}