Deep Network Design for Medical Image Computing: Principles and Applications

Deep Network Design for Medical Image Computing: Principles and Applications covers a range of MIC tasks and discusses design principles of these tasks for deep learning approaches in medicine. These include skin disease classification, vertebrae identification and localization, cardiac ultrasound image segmentation, 2D/3D medical image registration for intervention, metal artifact reduction, sparse-view artifact reduction, etc. For each topic, the book provides a deep learning-based solution that takes into account the medical or biological aspect of the problem and how the solution addresses a variety of important questions surrounding architecture, the design of deep learning techniques, when to introduce adversarial learning, and more. This book will help graduate students and researchers develop a better understanding of the deep learning design principles for MIC and to apply them to their medical problems. Explains design principles of deep learning techniques for MIC Contains cutting-edge deep learning research on MIC Covers a broad range of MIC tasks, including the classification, detection, segmentation, registration, reconstruction and synthesis of medical images INDICE: 1. Introduction2. Deep Learning Basics and Design PrinciplesPart I: Deep Network Design for Medical Image Analysis and Selected Applications3. Classification: Skin Lesion and Disease Recognition4. Detection: Vertebrae Localization and Identification5. Segmentation: Intracardiac Echocardiography Contouring6. Registration: 2D/3D Medical Image RegistrationPart II: Deep Network Design for Medical Image Reconstruction and Selected Applications7. Sparse-view CBCT Reconstruction8. Dual-Domain CT Metal Artifact Reduction9. Unsupervised CT Metal Artifact Reduction10. Concluding Remarks

• ISBN: 978-0-12-824383-1
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 225
• Fecha Publicación: 01/09/2022
• Nº Volúmenes: 1
• Idioma: Inglés