Deep Network Design for Medical Image Computing: Principles and Applications
- Length: 264 pages
- Edition: 1
- Language: English
- Publisher: Academic Press
- Publication Date: 2022-09-13
- ISBN-10: 012824383X
- ISBN-13: 9780128243831
- Sales Rank: #8055459 (See Top 100 Books)
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.
Cover image Title page Table of Contents Copyright Dedication List of figures Acknowledgments Chapter 1: Introduction Abstract 1.1. Medical image computing 1.2. Deep learning design principles 1.3. Chapter organization References Chapter 2: Deep learning basics Abstract 2.1. Convolutional neural networks 2.2. Recurrent neural networks 2.3. Deep image-to-image networks 2.4. Deep generative networks References Part 1: Deep network design for medical image analysis and selected applications Chapter 3: Classification: lesion and disease recognition Abstract 3.1. Design principles 3.2. Case study: skin disease classification versus skin lesion characterization 3.3. Case study: skin lesion classification with multitask learning 3.4. Summary References Chapter 4: Detection: vertebrae localization and identification Abstract 4.1. Design principles 4.2. Case study: vertebrae localization and identification 4.3. Summary References Chapter 5: Segmentation: intracardiac echocardiography contouring Abstract 5.1. Design principles 5.2. Case study: intracardiac echocardiography contouring 5.3. Summary References Chapter 6: Registration: 2D/3D rigid registration Abstract 6.1. Design principles 6.2. Case study: 2D/3D medical image registration 6.3. Summary References Part 2: Deep network design for medical image reconstruction, synthesis, and selected applications Chapter 7: Reconstruction: supervised artifact reduction Abstract 7.1. Design principles 7.2. Case study: sparse-view artifact reduction 7.3. Case study: metal artifact reduction 7.4. Summary References Chapter 8: Reconstruction: unsupervised artifact reduction Abstract 8.1. Design principles 8.2. Case study: metal artifact reduction 8.3. Summary References Chapter 9: Synthesis: novel radiography view synthesis Abstract 9.1. Design principles 9.2. Case study: novel radiography view synthesis 9.3. Summary References Chapter 10: Challenges and future directions Abstract 10.1. Challenges and open issues 10.2. Trends and future directions References Index
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