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This book constitutes the refereed proceedings of the 12th International Workshop, OMIA 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 27, 2025.
The 17 full papers presented in this book were carefully selected and reviewed from 33 submissions.This workshop aimed to bring together scientists, clinicians, and students from multiple disciplines in the growing ophthalmic image analysis community such as electronic engineering, computer science, mathematics, and medicine to discuss the latest advancements in the field.
List of contents
.- DVIA-Net: Dual-path Video Information Aggregation Network for Anterior Chamber Angle Analysis.
.- BEAM: Boosting Fundus Image Enhancement via Adapted Text-to-Image Models.
.- Multimodal Fusion Framework Using Contrastive Learning for Exposure Keratopathy.
.- GARD: Gamma-based Anatomical Restoration and Denoising for Retinal OCT.
.- Anomaly Detection in Anterior Eye Segment Using Self-Supervised Siamese Autoencoders.
.- Generalized Visual Field Pattern Discovery Using Archetypal Analysis.
.- Uncertainty-Aware Multimodal Fusion for Reliable Fundus Disease Classification Using a Vision-Language Foundation Model.
.- RetBench: Which Ophthalmic Foundation Model Performs Best and Why?.
.- On the Limits of Uncertainty-Aware Fine-Tuning for Robust Diabetic Retinopathy Screening.
.- Semi-Automated Retinal Microsurgery Video Annotation with SAM2: Comparative Analysis of Prompt Strategies.
.- Cross Domain Few Shot Learning for Intra-operative OCT Segmentation.
.- Reasoning-Enhanced Vision-Language Model for Interpretable Diabetic Retinopathy Detection in Ultra-Wide-Field Fundus Images.
.- PASO: A Multipurpose Porcine Anterior Segment Dataset Featuring Spectral and Reconstructed OCT Volume Scans and Surgical Instrument Segmentation Masks.
.- abVAE: Attribute-Based Booster Variational Autoencoder for Interpretable Latent Presentation in Optical Coherence Tomography of Glaucomatous Eyes.
.- Early CHD Detection from Retinal Fundus Scans using a Spatial Context-Aware Hierarchical Attention Framework.
.- Dataset, Baseline and Evaluation Design for GAVE Challenge.
.- UncEGA-Net: Uncertainty-Guided Edge Attention for Optic Nerve Segmentation in Ultrasound Images.
