Ophthalmic Medical Image Analysis : 10th International Workshop, OMIA 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings / / Bhavna Antony [and five others], editors

• Pubblicazione: Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023]; ©2023
• Edizione: First edition.
• Descrizione fisica: 1 online resource (174 pages)
• Disciplina: 006.31
• Soggetto topico: Machine learning
• Persona (resp. second.): AntonyBhavna
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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 -- 2.2 Consistency Regularization Loss -- 2.3 Vessel Extraction Network -- 3 Experiments and Results -- 3.1 Data Description -- 3.2 Implementation Details -- 3.3 Ablation Study -- 3.4 Comparisons with State-of-the-Art Methods -- 4 Conclusion -- References -- Adapting Segment Anything Model (SAM) for Retinal OCT -- 1 Introduction -- 2 Background and Related Works -- 3 Methods -- 4 Experimental Setup -- 4.1 Dataset and Evaluation -- 5 Results -- 6 Conclusion -- References -- Dual-Modality Grading of Keratoconus Severity Based on Corneal Topography and Clinical Indicators -- 1 Introduction -- 2 Method -- 2.1 Overall Architecture -- 2.2 Image Classifier on Corneal Topography -- 2.3 Data Classifier on Clinical Indicators -- 2.4 Fusion Strategy for Final Grading -- 3 Experiments and Results -- 3.1 Dataset. ; 3.2 Implementation Details -- 3.3 Evaluation Metrics -- 3.4 Results and Discussion -- 4 Conclusion -- References -- Automated Optic Disc Finder and Segmentation Using Deep Learning for Blood Flow Studies in the Eye -- 1 Introduction -- 2 Methods -- 2.1 Data Preparation -- 2.2 Model Training -- 2.3 Model Evaluations -- 3 Results -- 4 Discussion and Conclusion -- References -- Multi-relational Graph Convolutional Neural Networks for Carotid Artery Stenosis Diagnosis via Fundus Images -- 1 Introduction -- 2 Methodology -- 2.1 Feature Extraction Layer -- 2.2 Constructing Multi-graphs -- 2.3 M-GCNs -- 3 Experiments and Results -- 3.1 Dataset -- 3.2 Results -- 4 Conclusion -- References -- Pretrained Deep 2.5D Models for Efficient Predictive Modeling from Retinal OCT: A PINNACLE Study Report -- 1 Introduction -- 1.1 Related Work -- 2 Methods: Predictive Model from Retinal OCT Volume -- 3 Experiments -- 4 Results -- 5 Conclusion -- References -- A Structure-Consistency GAN for Unpaired AS-OCT Image Inpainting -- 1 Introduction -- 2 Proposed Method -- 2.1 Network Architecture -- 2.2 Objective Function -- 3 Experiments -- 4 Conclusion -- References -- STAGE Challenge: Structural-Functional Transition in Glaucoma Assessment Challenge in MICCAI 2023 -- 1 Introduction -- 2 Dataset -- 3 Baseline -- 4 Evaluation -- 5 Conclusion -- References -- Author Index.
• Titolo autorizzato: Ophthalmic Medical Image Analysis
• ISBN: 3-031-44013-7
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910746290603321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture notes in computer science ; ; Volume 14096.