Intro
Preface
Organization
Contents
Associations Between Retinal Microvasculature Changes and Gray Matter Volume in a Mid-Life Cohort at Risk of Developing Alzheimer's Disease
1 Introduction
2 Methodology
2.1 Dataset
2.2 Image Processing
2.3 Statistical Analysis
3 Results
4 Discussion
References
Improved Automatic Diabetic Retinopathy Severity Classification Using Deep Multimodal Fusion of UWF-CFP and OCTA Images
1 Introduction
2 Methods
2.1 Model Architecture
2.2 Fusion Strategy
2.3 Manifold Mixup
3 Experiments and Results

3.1 Dataset
3.2 Implementation Details
3.3 Results and Discussion
4 Conclusion
References
Auxiliary-Domain Learning for a Functional Prediction of Glaucoma Progression
1 Introduction
2 Methods
2.1 Data Acquisition
2.2 Baseline Deep Learning Model
2.3 Hard Parameter Sharing and Combined Loss Function
3 Experiments and Results
4 Discussion
5 Conclusion
References
QuickQual: Lightweight, Convenient Retinal Image Quality Scoring with Off-the-Shelf Pretrained Models
1 Introduction
2 Methods
2.1 EyeQ Dataset
2.2 QuickQual

2.3 RIQS Beyond 3-Way Classification: Fixed Prior Linearisation
2.4 QuickQual MEga Minified Estimator (QuickQual-MEME)
2.5 Evaluation
3 Results
3.1 QuickQual Performance on EyeQ
3.2 QuickQual-MEME Performance on Binary Task
3.3 Convenience and Speed
4 Discussion
References
Recurrent Self Fusion: Iterative Denoising for Consistent Retinal OCT Segmentation
1 Introduction
2 Method
3 Results
4 Discussion and Conclusions
References
UAU-Net: United Attention U-Shaped Network for the Segmentation of Pigment Deposits in Fundus Images of Retinitis Pigmentosa

1 Introduction
2 Method
2.1 Proposed Model
2.2 Dataset
2.3 Evaluation Metrics
3 Experiments and Results
3.1 Implementation Details
3.2 Ablation Study
3.3 Comparison Study
4 Conclusion
References
Glaucoma Progression Detection and Humphrey Visual Field Prediction Using Discriminative and Generative Vision Transformers
1 Introduction
2 Methods
2.1 Datasets
2.2 GP Detection Using TimeSformer
2.3 VF Prediction Using Generative ViT
3 Results and Discussion
3.1 GP Detection
3.2 VF Prediction Results

4 Discussion, Conclusions, and Future Directions
References
Utilizing Meta Pseudo Labels for Semantic Segmentation of Targeted Optic Nerve Features
1 Introduction
2 Meta Pseudo Labels
2.1 Implementation
3 Experimental Methods
3.1 Data Collection
3.2 Data Acquisition
3.3 Data Division
4 Results
4.1 Segmentation Results
4.2 Axon Counts
5 Discussion
5.1 Limitations
6 Conclusion
References
Privileged Modality Guided Network for Retinal Vessel Segmentation in Ultra-Wide-Field Images
1 Introduction
2 Methodology
2.1 Overall Architecture