Deep Learning for Medical Image Analysis, 2nd Edition 

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Editors: S. Kevin Zhou, Hayit Greenspan, Dinggang Shen

ISBN: 9780323851244

Imprint: Academic Press

Published: November 23, 2023

Pages: 600

Paperback

Description

Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis. 

Key features

· Covers common research problems in medical image analysis and their challenges

· Describes the latest deep learning methods and the theories behind approaches for medical image analysis

· Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment

· Includes a Foreword written by Nicholas Ayache

Readership

Academic and industry researchers and graduate students in medical imaging, computer vision, biomedical engineering, Clinicians, radiographers

Table of contents

Cover image

Title page

Table of Contents

Copyright

Contributors

Foreword

Part 1: Deep learning theories and architectures

Chapter 1: An introduction to neural networks and deep learning

Abstract

1.1. Introduction

1.2. Feed-forward neural networks

1.3. Convolutional neural networks

1.4. Recurrent neural networks

1.5. Deep generative models

1.6. Tricks for better learning

1.7. Open-source tools for deep learning

References

Chapter 2: Deep reinforcement learning in medical imaging

Abstract

2.1. Introduction

2.2. Basics of reinforcement learning

2.3. DRL in medical imaging

2.4. Future perspectives

2.5. Conclusions

References

Chapter 3: CapsNet for medical image segmentation

Abstract

Acknowledgements

3.1. Convolutional neural networks: limitations

3.2. Capsule network: fundamental

3.3. Capsule network: related work

3.4. CapsNets in medical image segmentation

3.5. Discussion

References

Chapter 4: Transformer for medical image analysis

Abstract

4.1. Introduction

4.2. Medical image segmentation

4.3. Medical image classification

4.4. Medical image detection

4.5. Medical image reconstruction

4.6. Medical image synthesis

4.7. Discussion and conclusion

References

Part 2: Deep learning methods

Chapter 5: An overview of disentangled representation learning for MR image harmonization

Abstract

Acknowledgements

5.1. Introduction

5.2. IIT and disentangled representation learning

5.3. Unsupervised harmonization with supervised IIT

5.4. Conclusions

References

Chapter 6: Hyper-graph learning and its applications for medical image analysis

Abstract

6.1. Introduction

6.2. Preliminary of hyper-graph

6.3. Hyper-graph neural networks

6.4. Hyper-graph learning for medical image analysis

6.5. Application 1: hyper-graph learning for COVID-19 identification using CT images

6.6. Application 2: hyper-graph learning for survival prediction on whole slides histopathological images

6.7. Conclusions

References

Chapter 7: Unsupervised domain adaptation for medical image analysis

Abstract

7.1. Introduction

7.2. Image space alignment

7.3. Feature space alignment

7.4. Experiments

7.5. Output space alignment

7.6. Conclusion

References

Part 3: Medical image reconstruction and synthesis

Chapter 8: Medical image synthesis and reconstruction using generative adversarial networks

Abstract

8.1. Introduction

8.2. Types of GAN

8.3. Applications of GAN for medical imaging

8.4. Summary

References

Chapter 9: Deep learning for medical image reconstruction

Abstract

9.1. Introduction

9.2. Deep learning for MRI reconstruction

9.3. Deep learning for CT reconstruction

9.4. Deep learning for PET reconstruction

9.5. Discussion and conclusion

References

Part 4: Medical image segmentation, registration, and applications

Chapter 10: Dynamic inference using neural architecture search in medical image segmentation

Abstract

10.1. Introduction

10.2. Related works

10.3. Data oriented medical image segmentation

10.4. Experiments

10.5. Ablation study

10.6. Additional experiments

10.7. Discussions

References

Chapter 11: Multi-modality cardiac image analysis with deep learning

Abstract

11.1. Introduction

11.2. Multi-sequence cardiac MRI based myocardial and pathology segmentation

11.3. LGE MRI based left atrial scar segmentation and quantification

11.4. Domain adaptation for cross-modality cardiac image segmentation

References

Chapter 12: Deep learning-based medical image registration

Abstract

12.1. Introduction

12.2. Deep learning-based medical image registration methods

12.3. Deep learning-based registration with semantic information

12.4. Concluding remarks

References

Chapter 13: Data-driven learning strategies for biomarker detection and outcome prediction in Autism from task-based fMRI

Abstract

13.1. Introduction

13.2. BrainGNN

13.3. LSTM-based recurrent neural networks for prediction in ASD

13.4. Causality and effective connectivity in ASD

13.5. Conclusion

References

Chapter 14: Deep learning in functional brain mapping and associated applications

Abstract

14.1. Introduction

14.2. Deep learning models for mapping functional brain networks

14.3. Spatio-temporal models of fMRI

14.4. Neural architecture search (NAS) of deep learning models on fMRI

14.5. Representing brain function as embedding

14.6. Deep fusion of brain structure-function in brain disorders

14.7. Conclusion

References

Chapter 15: Detecting, localizing and classifying polyps from colonoscopy videos using deep learning

Abstract

15.1. Introduction

15.2. Literature review

15.3. Materials and methods

15.4. Results and discussion

15.5. Conclusion

References

Chapter 16: OCTA segmentation with limited training data using disentangled representation learning

Abstract

16.1. Introduction

16.2. Related work

16.3. Method

16.4. Discussion and conclusion

References

Part 5: Others

Chapter 17: Considerations in the assessment of machine learning algorithm performance for medical imaging

Abstract

17.1. Introduction

17.2. Data sets

17.3. Endpoints

17.4. Study design

17.5. Bias

17.6. Limitations and future considerations

17.7. Conclusion

References

Index

About the editors

S. Kevin Zhou

S. Kevin Zhou, PhD is dedicated to research on medical image computing, especially analysis and reconstruction, and its applications in real practices. Currently, he is a Distinguished Professor and Founding Executive Dean of School of Biomedical Engineering, University of Science and Technology of China (USTC) and directs the Center for Medical Imaging, Robotics, Analytic Computing and Learning (MIRACLE). Dr. Zhou was a Principal Expert and a Senior R&D Director at Siemens Healthcare Research. He has been elected as a fellow of AIMBE, IAMBE, IEEE, MICCAI and NAI and serves the MICCAI society as a board member and treasurer.

Hayit Greenspan

Hayit Greenspan, PhD is focused on developing deep learning tools for medical image analysis, as well as their translation to the clinic. She is a Professor of Biomedical Engineering with the Faculty of Engineering at Tel-Aviv University (on Leave), and currently with the Department of Radiology and the AI and Human Health Department at the Icahn School of Medicine at Mount Sinai, NYC. She is the Director of the AI Core at the Biomedical Engineering and Imaging (BMEII) Institute and the Co-director of a new AI and emerging technologies PhD program at Mount Sinai. Dr. Greenspan is also a co-founder of RADLogics Inc., a startup company bringing AI tools to clinician support

Dinggang Shen

Dinggang Shen, PhD is a Professor and a Founding Dean with School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, and also a Co-CEO of United Imaging Intelligence (UII), Shanghai. He is a Fellow of IEEE, AIMBE, IAPR and MICCAI. He was a Jeffrey Houpt Distinguished Investigator and a Full Professor (Tenured) with the University of North Carolina at Chapel Hill (UNC-CH), Chapel Hill, NC, USA. His research interests include medical image analysis, computer vision and pattern recognition. He has published more than 1,500 peer-reviewed papers in the international journals and conference proceedings, with H-index 130 and over 70K citations.