Deep Learning for Medical Image Analysis 2nd Edition

Deep Learning for Medical Image Analysis, 2nd Edition by S. Kevin Zhou, Hayit Greenspan, and Dinggang Shen is an essential, up-to-date reference for anyone applying AI to clinical imaging. This authoritative edition brings the latest deep learning architectures, evaluation strategies, and clinical perspectives into a single, accessible resource—perfect for researchers, radiologists, biomedical engineers, and graduate students around the world.

Discover clear explanations of core techniques—convolutional neural networks, segmentation, classification, detection, and image registration—paired with discussions of robustness, interpretability, and regulatory considerations. New and expanded chapters cover transformer models, self-supervised learning, federated approaches for multi-center data, and the ethical and deployment challenges unique to healthcare settings. Practical case studies highlight applications across radiology, pathology, cardiology, and more, showing how algorithms move from development to clinical impact.

Designed to bridge theory and practice, this edition emphasizes reproducible evaluation, meaningful clinical metrics, and strategies for working with limited or biased datasets common in global healthcare environments. Whether you’re building algorithms for tumor segmentation, automated diagnosis, or longitudinal imaging studies, you’ll find step-by-step insight into model design, validation, and integration into clinical workflows.

Concise, well-illustrated, and written by leading experts, this book serves as both a textbook for advanced courses and a hands-on guide for professionals implementing AI solutions in hospitals and research centers across North America, Europe, Asia, and beyond. Elevate your medical imaging work with a resource that balances cutting-edge research and real-world applicability.

Order your copy today to stay ahead in the rapidly evolving field of medical image analysis and bring reliable, responsible AI into clinical practice.

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