Intro
Preface
Organization
Contents
From Picoscale Pathology to Decascale Disease: Image Registration with a Scattering Transform and Varifolds for Manipulating Multiscale Data
1 Introduction
2 Methods
2.1 Image Acquisition and Processing
2.2 Algorithm for Multimodal Registration with Damaged Tissue
2.3 Scattering Transform for Retaining High Resolution Texture
2.4 Varifold Measures for Modeling and Crossing Multiple Scales
3 Results
4 Discussion
References
Multi-scale Hybrid Transformer Networks: Application to Prostate Disease Classification

1 Introduction
1.1 Contribution
2 Methods
2.1 Model Comparison
3 Experiments and Results
3.1 Dataset
3.2 Pre-processing and Augmentation
3.3 Training
3.4 Results
4 Conclusion
References
Predicting Treatment Response in Prostate Cancer Patients Based on Multimodal PET/CT for Clinical Decision Support
1 Introduction
2 Methods
2.1 Pipeline Overview
2.2 Dataset and Ground Truth Annotation
2.3 Automated Segmentation
2.4 Therapy Response Prediction
3 Results
3.1 Segmentation
3.2 Therapy Response Prediction
4 Discussion
5 Conclusion

2.2 UNet Architecture and Training
2.3 Radiomics Workflow
3 Results
3.1 GTVt Segmentation
3.2 Prognosis Prediction
4 Discussion and Conclusions
References
Feature Selection for Privileged Modalities in Disease Classification
1 Introduction
2 Background
2.1 Learning Using Privileged Information
2.2 Mutual Information Feature Selection
3 Method
4 Experiments
4.1 Compared LUPI Models
4.2 Datasets
5 Results
5.1 Parkinson's Dataset
5.2 TMJ Osteoarthritis Dataset
6 Conclusions
References

Merging and Annotating Teeth and Roots from Automated Segmentation of Multimodal Images
1 Introduction
2 Materials
3 Related Work
3.1 Root Canal Segmentation Algorithm
3.2 3D Shape Analysis for Segmentation and Classification
4 Methods
4.1 Root Canal Segmentation Algorithm
4.2 Dental Model Segmentation Algorithm
4.3 Universal Labeling and Merging Algorithm
5 Results
5.1 Root Canal Segmentation
5.2 Universal Labeling and Merging Algorithm
6 Conclusion
References
Structure and Feature Based Graph U-Net for Early Alzheimer's Disease Prediction