Federated Learning for Medical Imaging

Auteur
Xiaoxiao Li is an Assistant Professor in the Electrical and Computer Engineering Department at the University of British Columbia (UBC). Before joining UBC, Dr. Li was a Postdoc Research Fellow in the Computer Science Department at Princeton University. Dr. Li obtained her PhD degree from Yale University in 2020. Dr. Li received her bachelor's degree from Zhejiang University in 2015. In the recent few years,Dr. Li has over 30 papers published in leading machine learning conferences and journals, including NeurIPS, ICML, ICLR, MICCAI, IPMI, BMVC, AAAI, and Medical Image Analysis. Her work has been recognized with the OHBM Merit Abstract Award, the MLMI Best Paper Award, and the DART Best Paper Award. In addition, she has received travel awards from NeurIPS/ICML/MICCAI/IPMI. Dr. Li has also organized a number of workshops on the topic of machine learning and healthcare. She is the Associate Editor of Frontiers in NeuroImaging and a reviewer for a number of international conferences and journals. Ziyue Xu joined NVIDIA as a Senior Scientist in 2018, before which he was a Staff Scientist and Lab Manager at National Institutes of Health. His research interests lie in the area of image analysis and computer vision with applications in biomedical and clinical imaging using shape modeling, graph methods, and machine learning. He has been working on medical AI for the past several years along with fellow researchers and clinicians.

Ziyue received his B.S. from Tsinghua University in 2006, and M.S./Ph.D. from the University of Iowa in 2009/2012. He is an Associate Editor for the journals, Computerized Medical Imaging and Graphics (CMIG), IEEE Transactions on Medical Imaging (TMI), Journal of Biomedical and Health Informatics (JBHI), and Computers in Biology and Medicine (CBM).Huazhu Fu works in the Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore.

Texte du rabat
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.

Contenu

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