Computer Vision for Medical Imaging Understanding

Computer Vision for Medical Imaging Understanding presents a rigorous synthesis of computer vision, deep learning, and medical imaging science, addressing the need for a structured, clinically relevant resource amid rapid AI advances. The book targets researchers, clinicians, and students seeking to translate state-of-the-art algorithms into reliable imaging tools. Topics span foundational theory, data preparation, and evaluation, through to practical deployments in diagnostics, organ delineation, and disease characterization. It emphasizes data privacy, federated learning, and explainable AI as essential components for trustworthy, scalable healthcare applications.

• Provides comprehensive coverage of AI and deep learning for medical imaging from fundamentals to advanced topics
• Delivers hands-on frameworks and implementation guidance across Python, TensorFlow, PyTorch, MATLAB, and more
• Integrates privacy-preserving AI, federated learning, and explainable AI for clinical adoption
• Demonstrates data handling, annotation, augmentation, and evaluation metrics for robust model development
• Enables deployment on edge devices and integration into clinical workflows