Deep Network Design for Medical Image Computing

In Deep Network Design for Medical Image Computing, the authors explore the intricate relationship between deep learning and medical image computing (MIC). The book delves into various MIC tasks, such as skin disease classification, vertebrae identification and localization, and cardiac ultrasound image segmentation. Each task is examined through the lens of design principles that are crucial for implementing effective deep learning solutions in medical contexts. By addressing the biological aspects of each problem, the authors provide a comprehensive understanding of how to leverage deep learning techniques to enhance medical imaging outcomes.

The text also discusses essential topics such as 2D/3D medical image registration for interventions, metal artifact reduction, and sparse-view artifact reduction. Each chapter presents a deep learning-based solution while raising important questions about architectural choices, the design of deep learning techniques, and the strategic introduction of adversarial learning. This thoughtful approach ensures that readers not only grasp the technicalities but also appreciate the practical implications of these methods in real-world medical scenarios.

Deep Network Design for Medical Image Computing serves as an invaluable resource for graduate students and researchers alike. It equips them with the necessary tools and insights to navigate the complexities of deep learning in MIC, ultimately empowering them to apply these principles to address pressing medical challenges effectively.