Federated learning medical imaging (6 Ergebnisse)

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the proceedings from the workshops BRIDGE+DeCaF 2025, held at the 28th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, which took place in Daejeon, South Korea in September 2025.The 16 papers presented in this volume were carefully reviewed and selected from the total submissions from the following workshops: First International Workshop on Bridging Regulatory Science and Medical Imaging Evaluation (BRIDGE 2025)6th MICCAI Workshop on Distributed, Collaborative and Federated Learning (DeCaF 2025).…

Paperback. Zustand: Brand New. 172 pages. 6.10x0.39x9.25 inches. In Stock.

Taschenbuch. Zustand: Neu. Bridging Regulatory Science and Medical Imaging Evaluation; and Distributed, Collaborative, and Federated Learning | First International Workshop, BRIDGE 2025, and 6th International Workshop, DeCaF 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23 and September 27, 2025, Proceedings | Ghada Zamzmi (u. a.) | Taschenbuch | Lecture Notes in Computer Science | vi | Englisch | 2025 | Springer | EAN 9783032056658 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Zustand: New. In English.

Zustand: 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 .

Taschenbuch. Zustand: 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.…