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
4 Pith papers cite this work. Polarity classification is still indexing.
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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.
REVEAL++ replaces discrete phenotypic groups with differentiable soft multi-positive weighting derived from intra-modality embeddings in contrastive learning, outperforming prior discrete and baseline methods on UK Biobank incident AD prediction.
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
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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.
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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.
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REVEAL++: Differentiable Phenotypic Grouping for Vision-Language Retinal Modeling of Alzheimer's Disease Risk
REVEAL++ replaces discrete phenotypic groups with differentiable soft multi-positive weighting derived from intra-modality embeddings in contrastive learning, outperforming prior discrete and baseline methods on UK Biobank incident AD prediction.
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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.