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.
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
