EyeBench-V2 is a new benchmark that evaluates retinal fundus enhancement models using downstream clinical tasks, generalization tests, and structured expert assessments to measure real diagnostic utility.
Ret-clip: A retinal im- age foundation model pre-trained with clinical diagnostic re- ports
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
baseline 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
baseline 1polarities
baseline 1representative citing papers
REVEAL uses vision-language alignment of retinal morphometry and clinical risk narratives plus group contrastive learning to predict AD and dementia about 8 years early.
CGSD framework reaches 87.5% accuracy and 0.731 macro F1 on APTOS 2019 by conditioning diffusion denoising on dot-product vectors from image features and DR-grade text descriptions.
citing papers explorer
-
Bridging Restoration and Diagnosis: A Comprehensive Benchmark for Retinal Fundus Enhancement
EyeBench-V2 is a new benchmark that evaluates retinal fundus enhancement models using downstream clinical tasks, generalization tests, and structured expert assessments to measure real diagnostic utility.
-
REVEAL: Multimodal Vision-Language Alignment of Retinal Morphometry and Clinical Risks for Incident AD and Dementia Prediction
REVEAL uses vision-language alignment of retinal morphometry and clinical risk narratives plus group contrastive learning to predict AD and dementia about 8 years early.
-
Cross-Modal Semantic-Enhanced Diffusion Framework for Diabetic Retinopathy Grading
CGSD framework reaches 87.5% accuracy and 0.731 macro F1 on APTOS 2019 by conditioning diffusion denoising on dot-product vectors from image features and DR-grade text descriptions.