Ophthalmic Medical Image Analysis

.- 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.