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Christian F. Baumgartner | Akateeminen Kirjakauppa

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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis : Second Intern
Tekijä: Carole H. Sudre (ed.); Hamid Fehri (ed.); Tal Arbel (ed.); Christian F. Baumgartner (ed.); Adrian Dalca (ed.); Ryuta Tanno
Kustantaja: Springer (2020)
Saatavuus: Noin 17-20 arkipäivää
EUR   49,60
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging : 4th International Workshop, UNSURE 2022, Held in Conju
Tekijä: Carole H. Sudre (ed.); Christian F. Baumgartner (ed.); Adrian Dalca (ed.); Chen Qin (ed.); Ryutaro Tanno (ed.); Van Leemput
Kustantaja: Springer (2022)
Saatavuus: Noin 17-20 arkipäivää
EUR   54,40
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging : 5th International Workshop, UNSURE 2023, Held in Conju
Tekijä: Carole H. Sudre (ed.); Christian F. Baumgartner (ed.); Adrian Dalca (ed.); Raghav Mehta (ed.); Chen Qin (ed.); Willi Wells
Kustantaja: Springer (2023)
Saatavuus: Noin 17-20 arkipäivää
EUR   57,30
    
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis : Second Intern
49,60 €
Springer
Sivumäärä: 222 sivua
Asu: Pehmeäkantinen kirja
Julkaisuvuosi: 2020, 06.10.2020 (lisätietoa)
Kieli: Englanti
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.



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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis : Second Internzoom
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