Medical Image Synthesis 1st Edition

Capture the future of medical imaging with Medical Image Synthesis, 1st Edition by Xiaofeng Yang — a concise, authoritative guide that brings together theory, algorithms, and practical applications for creating realistic medical images using advanced computational methods.

From the first pages you’ll find clear explanations of core principles—image formation, statistical models, and evaluation metrics—followed by contemporary techniques such as deep learning, generative adversarial networks (GANs), variational autoencoders, and domain adaptation. Practical chapters walk through synthesis for MRI, CT, PET, and ultrasound, addressing common clinical and research challenges like data scarcity, augmentation for training AI, modality translation, and privacy-preserving synthetic datasets.

This book is written for engineers, radiologists, computer scientists, and graduate students seeking actionable knowledge that bridges theory and real-world healthcare needs. Each section emphasizes robust evaluation, reproducibility, and ethical considerations relevant to hospitals, research labs, and industry teams across North America, Europe, and Asia. Rich examples and clear figures make complex methods accessible without sacrificing technical rigor.

If you need a single-volume reference to accelerate projects in medical imaging AI, improve model generalization, or explore synthetic data strategies for clinical workflows, Xiaofeng Yang’s Medical Image Synthesis delivers insight and practical guidance. Enhance your work in radiology and healthcare AI—order your copy today and start transforming how medical images are generated, validated, and applied.

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