Federated Learning for Medical Imaging

Federated Learning for Medical Imaging by Xiaoxiao Li, Ziyue Xu, and Huazhu Fu delivers a timely, practical guide to unlocking collaborative AI across hospitals and research centers without compromising patient privacy. If your work intersects radiology, healthcare data science, or clinical deployment of AI, this book grabs your attention with real-world urgency: how do we build powerful models when data can’t leave the source?

Inside, you’ll find a clear, accessible journey from core principles to production-ready considerations. The authors explain the mechanics of federated learning, model aggregation, communication efficiency, and privacy techniques such as secure aggregation and differential privacy, all tailored to common medical imaging modalities—MRI, CT, and X‑ray. Explanations are grounded in practical evaluation metrics, strategies for handling heterogeneity across sites, and approaches to maintain interpretability and clinical trust.

For hospital IT leaders, radiologists, academic researchers, and industry teams across Europe, North America, and Asia-Pacific, this book translates theory into actionable steps. Learn how to design federated pipelines, comply with GDPR and HIPAA frameworks, and overcome deployment hurdles in multi-center studies. Case-driven examples and thoughtful discussion of ethical and regulatory implications make it an indispensable roadmap for launching privacy-preserving AI initiatives in clinical settings.

Whether you’re exploring collaborative research or steering enterprise AI in healthcare, this authoritative guide equips you with the knowledge to move from pilot experiments to scalable, compliant deployments. Add Federated Learning for Medical Imaging to your professional library and lead the next wave of responsible, cross-institutional medical AI innovation.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.