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