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This book constitutes the refereed proceedings of the 11th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2024, held in conjunction with the 27th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, in October 2024.
The 16 papers presented in this book were carefully reviewed and selected from 31 submissions. The papers cover various topics such as computer-aided detection and diagnosis of disease; image analysis of novel ophthalmic imaging modalities; multimodal ophthalmic image analysis; ophthalmic image atlases; ophthalmic image analysis in animals; registration of ophthalmic images, including multimodal, segmentation of structures (e.g., vasculature, lesions, landmarks), combined analysis of images of the eye and other organs; validation; and/or crowd sourcing.
List of contents
Selective Functional Connectivity between Ocular Dominance Columns in the Primary Visual Cortex.- ETSCL: An Evidence Theory-Based Supervised Contrastive Learning Framework for Multi-modal Glaucoma Grading.- VNR-AV: Structural Post-processing for Retinal Arteries and Veins Segmentation.- Wavelet Deep Learning Network for Objective Retinal Functional Estimation from Multimodal Retinal Imaging.- Inter-Frame Sclera Vessel Rotation Tracking for Toric Intraocular Lens Implantation Navigation.- Data Heterogeneity-aware Personalized Federated Learning for Diagnosis.- MM-UNet: A Mixed MLP Architecture for Improved Ophthalmic Image Segmentation.- Coral-CVDs: A consistent ordinal regression model for cardiovascular diseases grading.- Affordable Deep Learning for Diagnosing Inherited and Common Retinal Diseases via Color Fundus Photography.- Comparative Analysis of Data Augmentation for Retinal OCT Biomarker Segmentation.- Advanced Diabetic Retinopathy Classification: Integrating Pathological Indicators Segmentation and Morphological Feature Analysis.- Masked Image Modelling for Retinal OCT Understanding.- A Dual-Stream Network for Langerhans' Cells Segmentation in CCM Images.- Formula-Driven Data Augmentation and Partial Retinal Layer Copying for Retinal Layer Segmentation.- Enhancing Community Vision Screening: AI-Driven Retinal Photography for Early Disease Detection and Patient Trust.- Enhancing Large Foundation Models to Identify Fundus Diseases Based on Contrastive Enhanced Low-Rank Adaptation Prompt.
