Deep Learning for Medical Image Analysis
- 1st Edition - January 30, 2017
- Latest edition
- Editors: S. Kevin Zhou, Hayit Greenspan, Dinggang Shen
- Language: English
Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the… Read more
Deep learning is providing exciting solutions for medical image analysis problems and is seen as 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 have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas.
Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis.
Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis.
- Covers common research problems in medical image analysis and their challenges
- Describes 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 Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc.
- Includes a Foreword written by Nicholas Ayache
Academic and industry researchers and graduate students in medical imaging, computer vision, biomedical engineering
Part I: Introduction
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. Deep Models
- 1.5. Tricks for Better Learning
- 1.6. Open-Source Tools for Deep Learning
- References
2. An Introduction to Deep Convolutional Neural Nets for Computer Vision
- Abstract
- 2.1. Introduction
- 2.2. Convolutional Neural Networks
- 2.3. CNN Flavors
- 2.4. Software for Deep Learning
- References
Part II: Medical Image Detection and Recognition
3. Efficient Medical Image Parsing
- Abstract
- 3.1. Introduction
- 3.2. Background and Motivation
- 3.3. Methodology
- 3.4. Experiments
- 3.5. Conclusion
- Disclaimer
- References
4. Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition
- Abstract
- 4.1. Introduction
- 4.2. Related Work
- 4.3. Methodology
- 4.4. Results
- 4.5. Discussion and Future Work
- References
5. Automatic Interpretation of Carotid Intima–Media Thickness Videos Using Convolutional Neural Networks
- Abstract
- Acknowledgement
- 5.1. Introduction
- 5.2. Related Work
- 5.3. CIMT Protocol
- 5.4. Method
- 5.5. Experiments
- 5.6. Discussion
- 5.7. Conclusion
- References
6. Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical Images
- Abstract
- Acknowledgements
- 6.1. Introduction
- 6.2. Method
- 6.3. Mitosis Detection from Histology Images
- 6.4. Cerebral Microbleed Detection from MR Volumes
- 6.5. Discussion and Conclusion
- References
7. Deep Voting and Structured Regression for Microscopy Image Analysis
- Abstract
- Acknowledgements
- 7.1. Deep Voting: A Robust Approach Toward Nucleus Localization in Microscopy Images
- 7.2. Structured Regression for Robust Cell Detection Using Convolutional Neural Network
- References
Part III: Medical Image Segmentation
8. Deep Learning Tissue Segmentation in Cardiac Histopathology Images
- Abstract
- 8.1. Introduction
- 8.2. Experimental Design and Implementation
- 8.3. Results and Discussion
- 8.4. Concluding Remarks
- Notes
- Disclosure Statement
- Funding
- References
9. Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching
- Abstract
- 9.1. Background
- 9.2. Proposed Method
- 9.3. Experiments
- 9.4. Conclusion
- References
10. Characterization of Errors in Deep Learning-Based Brain MRI Segmentation
- Abstract
- 10.1. Introduction
- 10.2. Deep Learning for Segmentation
- 10.3. Convolutional Neural Network Architecture
- 10.4. Experiments
- 10.5. Results
- 10.6. Discussion
- 10.7. Conclusion
- References
Part IV: Medical Image Registration
11. Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning
- Abstract
- 11.1. Introduction
- 11.2. Proposed Method
- 11.3. Experiments
- 11.4. Conclusion
- References
12. Convolutional Neural Networks for Robust and Real-Time 2-D/3-D Registration
- Abstract
- 12.1. Introduction
- 12.2. X-Ray Imaging Model
- 12.3. Problem Formulation
- 12.4. Regression Strategy
- 12.5. Feature Extraction
- 12.6. Convolutional Neural Network
- 12.7. Experiments and Results
- 12.8. Discussion
- Disclaimer
- References
Part V: Computer-Aided Diagnosis and Disease Quantification
13. Chest Radiograph Pathology Categorization via Transfer Learning
- Abstract
- Acknowledgements
- 13.1. Introduction
- 13.2. Image Representation Schemes with Classical (Non-Deep) Features
- 13.3. Extracting Deep Features from a Pre-Trained CNN Model
- 13.4. Extending the Representation Using Feature Fusion and Selection
- 13.5. Experiments and Results
- 13.6. Conclusion
- References
14. Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of Lesions
- Abstract
- Acknowledgements
- 14.1. Introduction
- 14.2. Literature Review
- 14.3. Methodology
- 14.4. Materials and Methods
- 14.5. Results
- 14.6. Discussion
- 14.7. Conclusion
- References
15. Randomized Deep Learning Methods for Clinical Trial Enrichment and Design in Alzheimer's Disease
- Abstract
- Acknowledgements
- 15.1. Introduction
- 15.2. Background
- 15.3. Optimal Enrichment Criterion
- 15.4. Randomized Deep Networks
- 15.5. Experiments
- 15.6. Discussion
- References
Part VI: Others
16. Deep Networks and Mutual Information Maximization for Cross-Modal Medical Image Synthesis
- Abstract
- Acknowledgements
- 16.1. Introduction
- 16.2. Supervised Synthesis Using Location-Sensitive Deep Network
- 16.3. Unsupervised Synthesis Using Mutual Information Maximization
- 16.4. Conclusions and Future Work
- References
17. Natural Language Processing for Large-Scale Medical Image Analysis Using Deep Learning
- Abstract
- Acknowledgements
- 17.1. Introduction
- 17.2. Fundamentals of Natural Language Processing
- 17.3. Neural Language Models
- 17.4. Medical Lexicons
- 17.5. Predicting Presence or Absence of Frequent Disease Types
- 17.6. Conclusion
- References
- Edition: 1
- Latest edition
- Published: January 30, 2017
- Language: English
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