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Found 9 papers, 1 papers with code
EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis
Early detection of eye diseases like glaucoma, macular degeneration, and diabetic retinopathy is crucial for preventing vision loss.
Fundus2Video: Cross-Modal Angiography Video Generation from Static Fundus Photography with Clinical Knowledge Guidance
In this work, we pioneer dynamic FFA video generation from static CF images.
UWF-RI2FA: Generating Multi-frame Ultrawide-field Fluorescein Angiography from Ultrawide-field Retinal Imaging Improves Diabetic Retinopathy Stratification
The DeepDRiD dataset was used to externally assess the contribution of generated UWF-FA images to DR classification, using area under the receiver operating characteristic curve (AUROC) as outcome metrics.
Choroidal Vessel Segmentation on Indocyanine Green Angiography Images via Human-in-the-Loop Labeling
Thus, the study aims to develop a high-precision choroidal vessel segmentation model with limited labor using HITL framework.
EyeFound: A Multimodal Generalist Foundation Model for Ophthalmic Imaging
EyeFound provides a generalizable solution to improve model performance and lessen the annotation burden on experts, facilitating widespread clinical AI applications for retinal imaging.
Evaluating large language models in medical applications: a survey
Large language models (LLMs) have emerged as powerful tools with transformative potential across numerous domains, including healthcare and medicine.
EyeGPT: Ophthalmic Assistant with Large Language Models
Artificial intelligence (AI) has gained significant attention in healthcare consultation due to its potential to improve clinical workflow and enhance medical communication.
Camera Adaptation for Fundus-Image-Based CVD Risk Estimation
Most high-quality retinography databases ready for research are collected from high-end fundus cameras, and there is a significant domain discrepancy between different cameras.
Medical Visual Question Answering: A Survey
Medical Visual Question Answering~(VQA) is a combination of medical artificial intelligence and popular VQA challenges.
