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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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Request a sales quoteDeep 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.
- 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
Academic and industry researchers and graduate students in medical imaging, computer vision, biomedical engineering, Clinicians, radiographers
- 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
- No. of pages: 600
- Language: English
- Edition: 2
- Published: November 23, 2023
- Imprint: Academic Press
- Paperback ISBN: 9780323851244
- eBook ISBN: 9780323858885
SZ
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, USAHG
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, IsraelDS
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, USARead Deep Learning for Medical Image Analysis on ScienceDirect