Deep Learning for Medical Image Analysis

2nd Edition - November 23, 2023
Editors: S. Kevin Zhou, Hayit Greenspan, Dinggang Shen
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 8 5 1 2 4 - 4
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 8 5 8 8 8 - 5

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 learni… Read more

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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.

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

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..

Affiliations and expertise

Principal Key Expert, Medical Image Analysis, Siemens Healthcare Technology Center, Princeton, New Jersey, USA

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

Affiliations and expertise

Head, Medical Image Processing and Analysis Lab, Biomedical Engineering Department, Faculty of Engineering, Tel-Aviv University, Israel

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.

Affiliations and expertise

Professor, Department of Radiology and BRIC, UNC-Chapel Hill, USA

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Deep Learning for Medical Image Analysis

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S. Kevin Zhou

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Dinggang Shen