Federated Learning for Medical Imaging
Principles, Algorithms, and Applications
Huazhu Fu editor Ziyue Xu editor Xiaoxiao Li editor
Format:Paperback
Publisher:Elsevier Science Publishing Co Inc
Publishing:1st Mar '25
£109.00
This title is due to be published on 1st March, and will be despatched as soon as possible.
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. In addition, it 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 is a complete resource for computer scientists and engineers as well as clinicians and medical care policymakers wanting to learn about the application of federated learning to medical imaging.
ISBN: 9780443236419
Dimensions: unknown
Weight: unknown
260 pages