Deep Learning for Medical Image Analysis 1st Edition by Kevin Zhou 0128104090 9780128104095

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ISBN-10 :  0128104090

ISBN-13 :  9780128104095

Author : S. Kevin Zhou  

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 1st Table of contents:

Part I: Introduction
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. Deep Models
1.5. Tricks for Better Learning
1.6. Open-Source Tools for Deep Learning
References
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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

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