Intro
Preface
Organization
Contents
Increasing the Accessibility of Peripheral Artery Disease Screening with Deep Learning
1 Problem
2 Related Work
3 Data Collection Study
4 System Development
5 Validation Study
6 Conclusion
References
Deep Learning Meets Computational Fluid Dynamics to Assess CAD in CCTA
1 Introduction
2 Automated Assessment of CAD in CCTA
2.1 Straightened Representation of the Coronary Vessels
2.2 Representing Ground-Truth Segmentation as a 3D Mesh
2.3 Segmentation of Vessels Using U-Nets in Upsampled CTTA

2.4 Blood Flow Simulation
3 Experimental Validation
4 Conclusions and Future Work
References
Machine Learning for Dynamically Predicting the Onset of Renal Replacement Therapy in Chronic Kidney Disease Patients Using Claims Data
1 Introduction
2 Methods
2.1 Dataset Description
2.2 Task Definition
2.3 Data Representation and Processing
2.4 Model Description
2.5 Model Evaluation
3 Experiments and Results
3.1 Study Population and Dataset
3.2 Model Performance
4 Conclusions
References

Uncertainty-Aware Geographic Atrophy Progression Prediction from Fundus Autofluorescence
1 Introduction
2 Method
2.1 Data
2.2 Model Development
2.3 Uncertainty Estimation Using Deep Ensemble
3 Results
4 Conclusions
References
Automated Assessment of Renal Calculi in Serial Computed Tomography Scans
1 Introduction
1.1 Our Contributions
2 Materials and Methods
2.1 Data
2.2 Calculi Detection and Segmentation
2.3 Registration and Stone Matching
2.4 Manual Review and Tracking
2.5 Evaluation of Performance
2.6 Statistical Analysis
3 Results

3.1 Cohort Characteristics
3.2 Performance of the Stone Detection and Segmentation
3.3 Performance of Stone Tracking
4 Discussion
References
Prediction of Mandibular ORN Incidence from 3D Radiation Dose Distribution Maps Using Deep Learning
1 Introduction
2 Methods and Materials
2.1 Data
2.2 Prediction Models
2.3 Model Evaluation
2.4 Statistical Analysis
3 Results
4 Discussion
4.1 ORN Prediction
4.2 Study Limitations and Future Work
5 Conclusion
References
Analysis of Potential Biases on Mammography Datasets for Deep Learning Model Development

1 Introduction
2 Materials and Methods
2.1 Mammography Dataset
2.2 Bias Analysis
2.3 Bias Correction Techniques
2.4 Experimental Setup
3 Results and Discussion
4 Conclusions
References
ECG-ATK-GAN: Robustness Against Adversarial Attacks on ECGs Using Conditional Generative Adversarial Networks
1 Introduction
2 Methodology
2.1 Generator and Discriminator
2.2 Objective Function and Individual Losses
2.3 Adversarial Attacks
3 Experiments
3.1 Data Set Preparation
3.2 Hyper-parameters
3.3 Quantitative Evaluation
3.4 Qualitative Evaluation