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Federated Learning for Medical Imaging: Principles, Algorithms, and Applications (The MICCAI Society book Series) - Tapa blanda
Sinopsis
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.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca de los autores
Xiaoxiao Li is Assistant Professor, Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada.
Ziyue Xu, Senior Scientist, NVIDIA, Santa Clara, California, United States of America.
Huazhu Fu, Principal Scientist, Agency for Science, Technology and Research (A*STAR), Singapore.
"Sobre este título" puede pertenecer a otra edición de este libro.
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Federated Learning for Medical Imaging : Principles, Algorithms, and Applications
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Academic Press, 2025
ISBN 10: 0443236410
ISBN 13: 9780443236419
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Federated Learning for Medical Imaging: Principles, Algorithms, and Applications (The MICCAI Society book Series)
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Federated Learning for Medical Imaging : Principles, Algorithms, and Applications
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Federated Learning for Medical Imaging : Principles, Algorithms, and Applications
Publicado por
Academic Press, 2025
ISBN 10: 0443236410
ISBN 13: 9780443236419
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Tapa blanda
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Federated Learning for Medical Imaging : Principles, Algorithms, and Applications
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Federated Learning for Medical Imaging
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Elsevier Science Publishing Co Inc, US, 2025
ISBN 10: 0443236410
ISBN 13: 9780443236419
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Paperback. 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. Nº de ref. del artículo: LU-9780443236419
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Federated Learning for Medical Imaging
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Condición: 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 . Nº de ref. del artículo: 1527979907
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Federated Learning for Medical Imaging
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Taschenbuch. 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. Nº de ref. del artículo: 9780443236419
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Federated Learning for Medical Imaging
Publicado por
Elsevier Science Publishing Co Inc, US, 2025
ISBN 10: 0443236410
ISBN 13: 9780443236419
Nuevo
Paperback
Librería: Rarewaves.com UK, London, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. 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. Nº de ref. del artículo: LU-9780443236419
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