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.
1. Chapter 1: Introduction of federated learning - p. 3 2. Chapter 2: Fundamentals of federated learning - p. 9 3. Part 2: Advanced concepts and methods - p. 18 4. Introduction - p. 19 5. Chapter 3: Data heterogeneity: a curse and a blessing for federated learning - p. 21 6. Chapter 4: Personalized federated learning strategies - p. 33 7. Chapter 5: Federated learning on long-tailed datasets - p. 43 8. Chapter 6: Expanding the federated horizon: cross-domain techniques for collective intelligence - p. 57 9. Part 3: Trustworthy federated learning - p. 68 10. Introduction - p. 69 11. Chapter 7: Improving performance fairness in federated learning for heterogeneous medical images - p. 71 12. Chapter 8: Differential privacy - p. 83 13. Chapter 9: Utility-privacy tradeoff in federated learning - p. 95 14. Chapter 10: Reliable federated learning for disease detection - p. 109 15. Chapter 11: Federated noisy client learning - p. 121 16. Part 4: Real-world implementation and application - p. 136 17. Introduction - p. 137 18. Chapter 12: Real-world implementation and application of federated medical image segmentation - p. 139 19. Chapter 13: Enhancing MRI reconstruction with cross-silo federated learning - p. 155 20. Chapter 14: Real-world implementation and application - p. 173 21. Chapter 15: Summary and outlook - p. 183 22. Bibliography - p. 189 23. Index - p. 205
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.
1. Chapter 1: Introduction of federated learning - p. 3 2. Chapter 2: Fundamentals of federated learning - p. 9 3. Part 2: Advanced concepts and methods - p. 18 4. Introduction - p. 19 5. Chapter 3: Data heterogeneity: a curse and a blessing for federated learning - p. 21 6. Chapter 4: Personalized federated learning strategies - p. 33 7. Chapter 5: Federated learning on long-tailed datasets - p. 43 8. Chapter 6: Expanding the federated horizon: cross-domain techniques for collective intelligence - p. 57 9. Part 3: Trustworthy federated learning - p. 68 10. Introduction - p. 69 11. Chapter 7: Improving performance fairness in federated learning for heterogeneous medical images - p. 71 12. Chapter 8: Differential privacy - p. 83 13. Chapter 9: Utility-privacy tradeoff in federated learning - p. 95 14. Chapter 10: Reliable federated learning for disease detection - p. 109 15. Chapter 11: Federated noisy client learning - p. 121 16. Part 4: Real-world implementation and application - p. 136 17. Introduction - p. 137 18. Chapter 12: Real-world implementation and application of federated medical image segmentation - p. 139 19. Chapter 13: Enhancing MRI reconstruction with cross-silo federated learning - p. 155 20. Chapter 14: Real-world implementation and application - p. 173 21. Chapter 15: Summary and outlook - p. 183 22. Bibliography - p. 189 23. Index - p. 205
