3D Conditional GANs

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Table of contents

1 Introduction 
1.1 Context
1.1.1 Multiple Sclerosis
1.1.2 Multimodal Neuroimaging in Multiple Sclerosis
1.2 Deep Learning for Medical Image Prediction
1.2.1 Convolutional Neural Networks (CNNs)
1.2.2 Generative Adversarial Networks (GANs)
1.3 Thesis overview
2 FLAIR MR Image synthesis from Multisequence MRI using 3D Fully Convolutional Networks for Multiple Sclerosis 
2.1 Introduction
2.2 Method
2.2.1 3D Fully Convolutional Neural Networks
2.2.2 Pulse-sequence-specific Saliency Map (P3S Map)
2.2.3 Materials and Implementation Details
2.3 Experiments and Results
2.3.1 Model Parameters and Performance Trade-offs
2.3.2 Evaluation of Predicted Images
2.3.3 Pulse-Sequence-Specific Saliency Map (P3S Map) .
2.4 Discussion and Conclusion
3 Predicting PET-derived Demyelination from Multisequence MRI using Sketcher-Refiner Adversarial Training for Multiple Sclerosis 
3.1 Introduction
3.1.1 Related Work
3.1.2 Contributions
3.2 Method
3.2.1 Sketcher-Refiner Generative Adversarial Networks .
3.2.2 Adversarial Loss with Adaptive Regularization
3.2.3 Visual Attention Saliency Map
3.2.4 Network architectures
3.3 Experiments and Evaluations
3.3.1 Overview
3.3.2 Comparisons with state-of-the-art methods
3.3.3 Refinement Iteration Effect
3.3.4 Global Evaluation of Myelin Prediction
3.3.5 Voxel-wise Evaluation of Myelin Prediction
3.3.6 Attention in Neural Networks
3.3.7 Contribution of Multimodal MRI Images
3.4 Discussion
3.5 Conclusion
4 Predicting PET-derived Myelin Content from Multisequence MRI for Individual Longitudinal Analysis in Multiple Sclerosis 
4.1 Introduction
4.1.1 Related work
4.1.2 Contributions
4.2 Method
4.2.1 Overview
4.2.2 Conditional Flexible Self-Attention GAN (CF-SAGAN)
4.2.3 Adaptive Attention Regularization for MS Lesions
4.2.4 Clinical Longitudinal Dataset
4.2.5 Indices of Myelin Content Change
4.2.6 Network Architectures
4.3 Experiments and Evaluation
4.3.1 Implementation and Training Details
4.3.2 Evaluation of Global Image Quality
4.3.3 Evaluation of Adaptive Attention Regularization
4.3.4 Evaluation of Static Demyelination Prediction
4.3.5 Evaluation of Dynamic Demyelination and Remyelination Prediction
4.3.6 Clinical Correlation
4.4 Discussion
4.5 Conclusion
5 Conclusion and Perspectives 
5.1 Main Contributions
5.1.1 Predicting FLAIR MR Image from Multisequence MRI .
5.1.2 Predicting PET-derived Demyelination from Multisequence MRI
5.1.3 Predicting PET-derived Dynamic Myelin Changes from Multisequence MRI
5.2 Publications
5.3 Perspectives
5.3.1 Deep Learning for Medical Imaging Synthesis
5.3.2 Synthesized Data for Deep Learning
5.3.3 Interpretable Deep Learning for Clinical Usage
Bibliography