Federated Learning for Medical Imaging

Erscheinungsjahr 2025
ISBN: 978-0-443-23641-9
Verlag: Elsevier Inc

Buch, Englisch, 230 Seiten, Format (B × H): 193 mm x 234 mm, Gewicht: 490 g

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.

Autoren/Hrsg.

Li, Xiaoxia

Li, Xiaoxiao

Xu, Ziyue

Fu, Huazhu

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik

Inhaltsverzeichnis

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

Über Autor(innen)

Fu, Huazhu
Huazhu Fu, Principal Scientist, Agency for Science, Technology and Research (A*STAR), Singapore.

Xu, Ziyue
Ziyue Xu, Senior Scientist, NVIDIA, Santa Clara, California, United States of America.

Li, Xiaoxiao
Xiaoxiao Li is Assistant Professor, Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada.