Book contents
- Frontmatter
- Dedication
- Contents
- List of Contributors
- Preface
- Part I Theory of Deep Learning for Image Reconstruction
- 1 Formalizing Deep Neural Networks
- 2 Geometry of Deep Learning
- 3 Model-Based Reconstruction with Learning: From Unsupervised to Supervised and Beyond
- 4 Deep Algorithm Unrolling for Biomedical Imaging
- Part II Deep-Learning Architecture for Various Imaging Architectures
- Part III Generative Models for Biomedical Imaging
3 - Model-Based Reconstruction with Learning: From Unsupervised to Supervised and Beyond
from Part I - Theory of Deep Learning for Image Reconstruction
Published online by Cambridge University Press: 15 September 2023
Book contents
- Frontmatter
- Dedication
- Contents
- List of Contributors
- Preface
- Part I Theory of Deep Learning for Image Reconstruction
- 1 Formalizing Deep Neural Networks
- 2 Geometry of Deep Learning
- 3 Model-Based Reconstruction with Learning: From Unsupervised to Supervised and Beyond
- 4 Deep Algorithm Unrolling for Biomedical Imaging
- Part II Deep-Learning Architecture for Various Imaging Architectures
- Part III Generative Models for Biomedical Imaging
Summary
This chapter focuses on biomedical image reconstruction methods at the intersection of MBIR and machine learning. After briefly reviewing classical MBIR methods for image reconstruction, we discuss the combination of MBIR with unsupervised learning, supervised learning, or both. Such combinations offer potential advantages for learning even with limited data.
- Type
- Chapter
- Information
- Deep Learning for Biomedical Image Reconstruction , pp. 28 - 52Publisher: Cambridge University PressPrint publication year: 2023
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