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Añadir al carritopaperback. Condición: Very Good. Publisher overstock. May have remainder mark / minor shelfwear. 99% of orders arrive in 4-10 days. Discounted shipping on multiple books.
Librería: Majestic Books, Hounslow, Reino Unido
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 141,20
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Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 143,37
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Idioma: Inglés
Publicado por Elsevier Science Publishing Co Inc, 2025
ISBN 10: 0443236410 ISBN 13: 9780443236419
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
EUR 137,98
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Añadir al carritoPaperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 163,58
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 155,39
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 152,48
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 170,45
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Idioma: Inglés
Publicado por Elsevier Science Publishing Co Inc, US, 2025
ISBN 10: 0443236410 ISBN 13: 9780443236419
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
EUR 194,54
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Añadir al carritoPaperback. Condición: New. Federated Learning for Medical Imaging: Principles, Algorithms, and Applications gives a deep understanding of the technology of federated learning (FL), the architecture of a federated system, and the algorithms for FL. It shows how FL allows multiple medical institutes to collaboratively train and use a precise machine learning (ML) model without sharing private medical data via practical implantation guidance. The book includes real-world case studies and applications of FL, demonstrating how this technology can be used to solve complex problems in medical imaging. The book also provides an understanding of the challenges and limitations of FL for medical imaging, including issues related to data and device heterogeneity, privacy concerns, synchronization and communication, etc.This book is a complete resource for computer scientists and engineers, as well as clinicians and medical care policy makers, wanting to learn about the application of federated learning to medical imaging.
EUR 153,44
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Añadir al carritoCondición: New. Presents the specific challenges in developing and deploying FL to medical imagingExplains the tools for developing or using FLPresents the state-of-the-art algorithms in the field with open source software on GithubGives .
Idioma: Inglés
Publicado por Elsevier Science Publishing Co Inc, US, 2025
ISBN 10: 0443236410 ISBN 13: 9780443236419
Librería: Rarewaves.com UK, London, Reino Unido
EUR 183,15
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Añadir al carritoPaperback. Condición: New. Federated Learning for Medical Imaging: Principles, Algorithms, and Applications gives a deep understanding of the technology of federated learning (FL), the architecture of a federated system, and the algorithms for FL. It shows how FL allows multiple medical institutes to collaboratively train and use a precise machine learning (ML) model without sharing private medical data via practical implantation guidance. The book includes real-world case studies and applications of FL, demonstrating how this technology can be used to solve complex problems in medical imaging. The book also provides an understanding of the challenges and limitations of FL for medical imaging, including issues related to data and device heterogeneity, privacy concerns, synchronization and communication, etc.This book is a complete resource for computer scientists and engineers, as well as clinicians and medical care policy makers, wanting to learn about the application of federated learning to medical imaging.
Idioma: Inglés
Publicado por Elsevier Science Publishing Co Inc Jun 2025, 2025
ISBN 10: 0443236410 ISBN 13: 9780443236419
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 213,53
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware - Federated Learning for Medical Imaging: Principles, Algorithms, and Applications gives a deep understanding of the technology of federated learning (FL), the architecture of a federated system, and the algorithms for FL. It shows how FL allows multiple medical institutes to collaboratively train and use a precise machine learning (ML) model without sharing private medical data via practical implantation guidance. The book includes real-world case studies and applications of FL, demonstrating how this technology can be used to solve complex problems in medical imaging. The book also provides an understanding of the challenges and limitations of FL for medical imaging, including issues related to data and device heterogeneity, privacy concerns, synchronization and communication, etc.This book is a complete resource for computer scientists and engineers, as well as clinicians and medical care policy makers, wanting to learn about the application of federated learning to medical imaging.
Librería: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 115,50
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Añadir al carritoCondición: new. Questo è un articolo print on demand.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 125,51
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Añadir al carritoPaperback. Condición: Brand New. 260 pages. 9.25x7.50x9.22 inches. In Stock. This item is printed on demand.
Idioma: Inglés
Publicado por Elsevier Science Publishing Co Inc, San Diego, 2025
ISBN 10: 0443236410 ISBN 13: 9780443236419
Librería: CitiRetail, Stevenage, Reino Unido
EUR 128,18
Cantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: new. Paperback. Federated Learning for Medical Imaging: Principles, Algorithms, and Applications gives a deep understanding of the technology of federated learning (FL), the architecture of a federated system, and the algorithms for FL. It shows how FL allows multiple medical institutes to collaboratively train and use a precise machine learning (ML) model without sharing private medical data via practical implantation guidance. The book includes real-world case studies and applications of FL, demonstrating how this technology can be used to solve complex problems in medical imaging. The book also provides an understanding of the challenges and limitations of FL for medical imaging, including issues related to data and device heterogeneity, privacy concerns, synchronization and communication, etc.This book is a complete resource for computer scientists and engineers, as well as clinicians and medical care policy makers, wanting to learn about the application of federated learning to medical imaging. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.