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Federated Learning for Medical Imaging
Principles, Algorithms and Applications
1st Edition - October 1, 2024
Editors: Xiaoxiao Li, Ziyue Xu, Huazhu Fu
Language: English
Paperback ISBN:9780443236419
9 7 8 - 0 - 4 4 3 - 2 3 6 4 1 - 9
eBook ISBN:9780443236426
9 7 8 - 0 - 4 4 3 - 2 3 6 4 2 - 6
Federated Learning for Medical Imaging: principles, algorithms and applications gives a deep understanding of the technology of federated learning (FL), the architecture of a fede…Read more
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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.
Federated Learning for Medical Imaging: Principles, Algorithms and Applications 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
Academic and industry researchers in biomedical engineering, computer science and electronic engineering researching into medical imaging, Masters and PhD students studying and researching medical imaging
1 Background
2 2.1 – Introduction and Definitions
2.2 – Types of FL
2.3 – Frameworks and Algorithms
3 – Privacy and Security
3.1 – Differential Privacy
3.2 – Attacks and Defense in FL
4 – Advanced Algorithms
4.1 – Handling Heterogeneous Data in FL
4.2 – Ensuring Fairness in
4.3 – FL Optimization
4.4 – Other Methods
5 – Real-world Implementation and Application
5.1 – Disease Diagnosis and Prediction
5.2 – Image Segmentation
5.3 – Image Registration
5.4 – Image Reconstruction and Translation
5.5 – Image Enhancement
6 – Practical Guidance
6.1 – Platforms
6.2 – Open-source Libraries
6.3 – Datasets and Benchmark
6.3 – Real-world Implementation
7 – Summary and Outlook
No. of pages: 260
Language: English
Edition: 1
Published: October 1, 2024
Imprint: Academic Press
Paperback ISBN: 9780443236419
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Xiaoxiao Li
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.
Affiliations and expertise
Assistant Professor, Electrical and Computer Engineering Department, University of British Columbia, Vancouver, BC, Canada
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Ziyue Xu
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).
Affiliations and expertise
NVIDIA, Reston, VA, USA
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Huazhu Fu
Huazhu Fu works in the Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore.
Affiliations and expertise
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore