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.