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• Intro
• Preface
• Organization
• Contents
• AugPaste: One-Shot Anomaly Detection for Medical Images
• 1 Introduction
• 2 Methods
• 2.1 Construction of Lesion Bank
• 2.2 Synthesis of Anomalous Samples
• 2.3 Anomaly Detection Network
• 2.4 Implementation Details
• 3 Experiments and Results
• 3.1 Datasets
• 3.2 Evaluation Metric
• 3.3 Ablation Studies on EyeQ
• 3.4 Comparison with State-of-the-Art
• 4 Conclusion
• References
• Analysing Optical Coherence Tomography Angiography of Mid-Life Persons at Risk of Developing Alzheimer's Disease Later in Life
• 1 Introduction

• 2 Methodology
• 3 Results
• 3.1 Vessel Tortuosity Decreases in Risk Groups
• 3.2 Longitudinal Variations of Retinal Features in Risk Groups
• 4 Discussion
• 5 Conclusion
• References
• Feature Representation Learning for Robust Retinal Disease Detection from Optical Coherence Tomography Images
• 1 Introduction
• 2 Related Work
• 3 Methodology
• 3.1 Robust Feature Learning Architecture
• 3.2 Proposed Representation Learning Loss
• 3.3 Final Objective Function
• 4 Experiments
• 4.1 Data-Set Processing
• 4.2 Hyper-parameter Tuning
• 4.3 Performance Metrics

• 4.4 Quantitative Evaluation
• 4.5 Qualitative Evaluation
• 5 Conclusion and Future Work
• References
• GUNet: A GCN-CNN Hybrid Model for Retinal Vessel Segmentation by Learning Graphical Structures
• 1 Introduction
• 2 Method
• 2.1 GUNet
• 2.2 Graph Convolution
• 2.3 Graph Construction
• 3 Experiments
• 3.1 Datasets and Evaluation Metrics
• 3.2 Implementation Details
• 4 Results
• 4.1 Experiments on Fundus Photography
• 4.2 Experiments on SLO Images
• 4.3 Visualization
• 5 Conclusion
• References
• Detection of Diabetic Retinopathy Using Longitudinal
• 1 Introduction

• 2 Methods
• 2.1 Longitudinal Siamese
• 2.2 Longitudinal Self-supervised Learning
• 2.3 Longitudinal Neighbourhood Embedding
• 3 Dataset
• 4 Experiments and Results
• 4.1 Comparison of the Approaches on the Early Change Detection
• 4.2 Norm of Trajectory Vector Analyze
• 5 Discussion
• References
• Multimodal Information Fusion for Glaucoma and Diabetic Retinopathy Classification
• 1 Introduction
• 2 Methods
• 2.1 Early Fusion
• 2.2 Intermediate Fusion
• 2.3 Hierarchical Fusion
• 3 Material and Experiments
• 3.1 Data
• 3.2 Data Pre-processing
• 3.3 Implementation Details

• 4 Results
• 4.1 GAMMA Dataset
• 4.2 PlexEliteDR Dataset
• 5 Conclusion
• References
• Mapping the Ocular Surface from Monocular Videos with an Application to Dry Eye Disease Grading
• 1 Introduction
• 2 Proposed Method
• 3 Experiments and Results
• 3.1 SiGMoid
• 3.2 DED Diagnosis: Classification
• 4 Discussion and Conclusion
• References
• Rethinking Retinal Image Quality: Treating Quality Threshold as a Tunable Hyperparameter
• 1 Introduction
• 2 Methods
• 2.1 Quality Prediction on a Categorical Scale and Continuous Scale
• 2.2 Effect of Varying Image Quality Threshold

This book constitutes the refereed proceedings of the 9th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2022, held in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022, in Singapore, Singapore, in September 2022. The 20 papers presented at OMIA 2022 were carefully reviewed and selected from 33 submissions. The papers cover various topics in the field of ophthalmic medical image analysis and challenges in terms of reliability and validation, number and type of conditions considered, multi-modal analysis (e.g., fundus, optical coherence tomography, scanning laser ophthalmoscopy), novel imaging technologies, and the effective transfer of advanced computer vision and machine learning technologies.
(source: Nielsen Book Data)

• Expectations of Artificial Intelligence for Pathology.- Interpretable Deep Neural Network to Predict Estrogen Receptor Status from Haematoxylin-Eosin Images.- Supporting the Donation of Health Records to Biobanks for Medical Research.- Survey of XAI in Digital Pathology.- Sample Quality as Basic Prerequisite for Data Quality: A Quality Management System for Biobanks.- Black Box Nature of Deep Learning for Digital Pathology: Beyond Quantitative to Qualitative Algorithmic Performances.- Towards a Better Understanding of the Workflows: Modeling Pathology Processes in View of Future AI Integration.- OBDEX - Open Block Data Exchange System.- Image Processing and Machine Learning Techniques for Diabetic Retinopathy Detection: A Review.- Higher Education Teaching Material on Machine Learning in the Domain of Digital Pathology.- Classification vs Deep Learning in Cancer Degree on Limited Histopathology Datasets.- Biobanks and Biobank-Based Artificial Intelligence (AI) Implementation Through an International Lens.- HistoMapr: An Explainable AI (xAI) Platform for Computational Pathology Solutions.- Extension of the Identity Management System Mainzelliste to Reduce Runtimes for Patient Registration in Large Datasets.- Digital Image Analysis in Pathology Using DNA Stain: Contributions in Cancer Diagnostics and Development of Prognostic and Theranostic Biomarkers.- Assessment and Comparison of Colour Fidelity of Whole slide imaging scanners.- Deep Learning Methods for Mitosis Detection in Breast Cancer Histopathological Images: a Comprehensive Review.- Developments in AI and Machine Learning for Neuroimaging.
• (source: Nielsen Book Data)

Data driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance. However, in the context of medicine it is important for a human expert to verify the outcome. Consequently, there is a need for transparency and re-traceability of state-of-the-art solutions to make them usable for ethical responsible medical decision support. Moreover, big data is required for training, covering a wide spectrum of a variety of human diseases in different organ systems. These data sets must meet top-quality and regulatory criteria and must be well annotated for ML at patient-, sample-, and image-level. Here biobanks play a central and future role in providing large collections of high-quality, well-annotated samples and data. The main challenges are finding biobanks containing ''fit-for-purpose'' samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions.
(source: Nielsen Book Data)

• Adjacent Scale Fusion and Corneal Position Embedding for Corneal Ulcer Segmentation.- Longitudinal detection of diabetic retinopathy early severity grade changes using deep learning.- Intra-operative OCT (iOCT) Image Quality Enhancement: A Super-Resolution Approach using High Quality iOCT 3D Scans.- Diabetic Retinopathy Detection based on Weakly Supervised Object Localization and Knowledge Driven Attribute Mining.- FARGO: A Joint Framework for FAZ and RV Segmentation from OCTA Images.- CDLRS: Collaborative Deep Learning Model with Joint Regression and Segmentation for Automatic Fovea Localization.- U-Net with Hierarchical Bottleneck Attention for Landmark Detection in Fundus Images of the Degenerated Retina.- Radial U-Net: Improving DMEK Graft Detachment Segmentation in Radial AS-OCT Scans.- Guided Adversarial Adaptation Network for Retinal and Choroidal Layer Segmentation.- Juvenile Refractive Power Prediction based on Corneal Curvature and Axial Length via a Domain Knowledge Embedding Network.- Peripapillary Atrophy Segmentation with Boundary Guidance.- Are cardiovascular risk scores from genome and retinal image complementary? A deep learning investigation in a diabetic cohort.- Dual-branch Attention Network and Atrous Spatial Pyramid Pooling for Diabetic Retinopathy Classification Using Ultra-Widefield Images.- Self-Adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images.- Multi-Modality Images Analysis: A Baseline for Glaucoma Grading via Deep Learning.- Impact of data augmentation on retinal OCT image segmentation for diabetic macular edema analysis.- Representation and Reconstruction of Image-Based Structural Patterns of Glaucomatous Defects Using Only Two Latent Variables from a Variational Autoencoder.- Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification.- Attention Guided Slit Lamp Image Quality Assessment.- Robust Retinal Vessel Segmentation from a Data Augmentation Perspective.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 8th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2021, held in conjunction with the 24th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2021, in Strasbourg, France, in September 2021.* The 20 papers presented at OMIA 2021 were carefully reviewed and selected from 31 submissions. The papers cover various topics in the field of ophthalmic medical image analysis and challenges in terms of reliability and validation, number and type of conditions considered, multi-modal analysis (e.g., fundus, optical coherence tomography, scanning laser ophthalmoscopy), novel imaging technologies, and the effective transfer of advanced computer vision and machine learning technologies. *The workshop was held virtually.
(source: Nielsen Book Data)