Federated Learning for Medical Imaging

Name: Federated Learning for Medical Imaging
Brand: Elsevier Science Publishing Co Inc
Price: 335.75 AUD
Availability: PreOrder

Principles, Algorithms and Applications

By: Xiaoxiao Li (Editor), Ziyue Xu (Editor), Huazhu Fu (Editor)

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Paperback | 1 March 2025

At a Glance

Paperback
260 Pages

Dimensions(cm)
23.5 x 19.05

Paperback

$335.75

or 4 interest-free payments of $83.94 with

Available: 1st March 2025

Preorder. Will ship when available.

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.

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