Long description:

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.

• Presents the specific challenges in developing and deploying FL to medical imaging
• Explains the tools for developing or using FL
• Presents the state-of-the-art algorithms in the field with open source software on Github
• Gives insight into potential issues and solutions of building FL infrastructures for real-world application
• Informs researchers on the future research challenges of building real-world FL applications

Table of Contents:

Section I Fundamentals of FL
1. Background
2. FL Foundations

Section II Advanced Concepts and Methods for Heterogenous Settings
3. FL on Heterogeneous Data
4. FL on long-tail (label)
5. Personalized FL
6. Cross-domain FL

Section III Trustworthy FL
7. FL and Fairness
8. Differential Privacy
9. Security (Attack and Defense) in FL
10. FL + Uncertainty
11. Noisy learning in FL

Section IV Real-world Implementation and Application
12. Image Segmentation
13. Image Reconstruction and Registration
14. Frameworks and Platforms

Section V Afterword
15. Summary and Outlook