Intro
Preface
Organization
Contents
Bio-inspired Attentive Segmentation of Retinal OCT Imaging
1 Introduction
2 Methods
2.1 Bio-inspired Attentive Segmentation
2.2 Low-Rank Oriented Attention (LROA)
2.3 Architectural Overview
3 Experiments and Results
3.1 Data
3.2 Experimental Setup
3.3 Results
4 Discussion and Conclusion
References
DR Detection Using Optical Coherence Tomography Angiography (OCTA):pg A Transfer Learning Approach with Robustness Analysis
1 Introduction
2 Methods
2.1 Datasets and Imaging Devices

2.2 Data Augmentation and Transfer Learning
3 Results
3.1 Classification of Controls, DR and NoDR Patients
3.2 Model Validation on OCTAGON Dataset
4 Discussion and Conclusions
References
What is the Optimal Attribution Method for Explainable Ophthalmic Disease Classification?
1 Introduction
2 Related Studies
3 Methods
4 Analysis
4.1 Quantitative Analysis
4.2 Qualitative Analysis
5 Conclusion
References
DeSupGAN: Multi-scale Feature Averaging Generative Adversarial Network for Simultaneous De-blurring and Super-Resolution of Retinal Fundus Images

1 Introduction
2 Related Work
3 Methodology
3.1 DeSupGAN Structure
3.2 Loss Functions
4 Experiment
4.1 Dataset Generation
4.2 Training Details
4.3 Results
4.4 Ablation Studies
5 Conclusion
References
Encoder-Decoder Networks for Retinal Vessel Segmentation Using Large Multi-scale Patches
1 Introduction
2 Studying Patch Size and Model Architecture
2.1 Effective Patch Sizes
2.2 Efficient Architecture
3 Comparison with State-of-the-Art
3.1 Results
3.2 High-Resolution Fundus Images
3.3 Cross-Dataset Evaluation
4 Conclusion
References

Retinal Image Quality Assessment via Specific Structures Segmentation
1 Introduction
2 Database
3 Method
3.1 Segmentation Modules
3.2 Quality Assessment Module
3.3 Implementation Detail
4 Results
4.1 Comparative Studies
4.2 Ablation Studies
4.3 Computational Complexity
5 Conclusion
References
Cascaded Attention Guided Network for Retinal Vessel Segmentation
1 Introduction
2 Methodology
2.1 Cascaded Deep Learning Network
2.2 Attention UNet++
3 Experiments
3.1 Datasets
3.2 Implementation Details
3.3 Evaluation Methods
3.4 Results

3.5 Ablation Study
4 Conclusion
References
Self-supervised Denoising via Diffeomorphic Template Estimation: Application to Optical Coherence Tomography
1 Introduction
2 Methods
2.1 Problem Formulation
2.2 Registration
2.3 Denoising
3 Experiments
3.1 Dataset
3.2 Implementation Details
3.3 Evaluation Methods
3.4 Results
4 Discussion
References
Automated Detection of Diabetic Retinopathy from Smartphone Fundus Videos
1 Introduction
2 Materials and Methods
2.1 Data Acquisition and Annotation
2.2 Cropping Frames to the Lens