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Condizione: New. 1st ed. 2023 edition NO-PA16APR2015-KAP.
Lingua: Inglese
Editore: Springer-Nature New York Inc, 2024
ISBN 10: 3031472918 ISBN 13: 9783031472916
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the proceedings of the 14th International Workshop, CDMRI 2023, held in conjunction with MICCAI 2023, the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention. The conference took place in Vancouver, BC, Canada, on October 8, 2023.The 17regular papers presented in this book were carefully reviewed and selected from 19 submissions. These contributions cover various aspects, including preprocessing, signal modeling, tractography, bundle segmentation, and clinical applications. Many of these studies employ novel machine learning implementations, highlighting the evolving landscape of techniques beyond the more traditional physics-based algorithms.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Computational Diffusion MRI | 14th International Workshop, CDMRI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings | Muge Karaman (u. a.) | Taschenbuch | Lecture Notes in Computer Science | x | Englisch | 2024 | Springer | EAN 9783031472916 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Aggiungi al carrelloCondizione: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book constitutes the proceedings of the 14th International Workshop, CDMRI 2023, held in conjunction with MICCAI 2023, the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention. The conference took place in Vancouver, BC, Canada, on October 8, 2023. The 17regular papers presented in this book were carefully reviewed and selected from 19 submissions. These contributions cover various aspects, including preprocessing, signal modeling, tractography, bundle segmentation, and clinical applications. Many of these studies employ novel machine learning implementations, highlighting the evolving landscape of techniques beyond the more traditional physics-based algorithms.
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