Deep Network Design for Medical Image Computing
Principles and Applications

Helps readers deep learning design methods specifically developed to solve medical problems

Haofu Liao (Author), S. Kevin Zhou (Author), Jiebo Luo (Author)

9780128243831, Elsevier Science

Paperback, published 30 August 2022

264 pages, 75 illustrations (30 in full color)
23.5 x 19 x 1.8 cm, 0.52 kg

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.

1. Introduction
2. Deep Learning Basics
3. Classification: Lesion and Disease Recognition
4. Detection: Vertebrae Localization and Identification
5. Segmentation: Intracardiac Echocardiography Contouring
6. Registration: 2D/3D Medical Image Registration
7. Reconstruction: Supervised Artifact Reduction
8. Reconstruction: Unsupervised Artifact Reduction
9. Synthesis: Novel View Synthesis
10. Challenges and Future Directions

Subject Areas: Computer vision [UYQV], Neural networks & fuzzy systems [UYQN], Machine learning [UYQM], Artificial intelligence [UYQ]