Deep Network Design for Medical Image Computing

Deep Network Design for Medical Image Computing by Haofu Liao is an essential guide for anyone building robust deep-learning solutions for medical imaging. Clear, authoritative, and practical, this book translates complex theory into actionable design choices, helping clinicians, researchers, and engineers bridge the gap between algorithm and clinical impact.

Begin with a compelling overview of modern challenges in medical image computing—noisy scans, limited labels, multi-modal data—and discover why careful network design matters more than off-the-shelf models. Liao breaks down architecture patterns, optimization strategies, loss functions, and evaluation metrics with a focus on real-world tasks such as segmentation, classification, detection, and image synthesis for MRI, CT, ultrasound, and histopathology.

Readers will gain insight into designing networks that generalize across hospitals and patient populations, improving robustness, interpretability, and clinical utility. Practical discussions cover data augmentation, transfer learning, domain adaptation, and integration into clinical workflows, making the book valuable for deep-learning practitioners in radiology departments, biomedical research centers, and healthcare startups worldwide.

With a balanced mix of theory, case studies, and design checklists, this title sharpens your intuition for model selection, architecture customization, and performance trade-offs. Whether you are a graduate student, medical data scientist, or practicing radiologist exploring AI, Haofu Liao’s guidance helps you build models that are both scientifically rigorous and clinically relevant.

Comprehensive, contemporary, and globally applicable—add Deep Network Design for Medical Image Computing by Haofu Liao to your library and take a decisive step toward creating safer, more effective AI tools for healthcare. Order your copy today.

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