Federated Learning for Medical Imaging Principles, Algorithms and Applications

Federated Learning for Medical ImagingPrinciples, Algorithms, and Applications\nAuthor(s): Xiaoxiao Li, Ziyue Xu, Huazhu Fu\nFormat: Paperback\nPublisher: Elsevier Science Publishing Co Inc, United States\nImprint: Academic Press Inc\nISBN-13: 9780443236419, 978-0443236419\nSynopsis\nFederated 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 .