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.
MedCLIP: Contrastive Learning from Unpaired Medical Images and Text.Proc Conf Empir Methods Nat Lang Process
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Mean pooling and multi-window RGB encoding optimize vision-language performance on CT enterography, with retrieval-augmented generation substantially improving automated report severity accuracy over fine-tuning alone.
MIRAGE combines a medical CLIP model, a diffusion generator, and an LLM into an accessible interface for retrieving and creating educational medical images and texts.
citing papers explorer
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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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Representation geometry shapes task performance in vision-language modeling for CT enterography
Mean pooling and multi-window RGB encoding optimize vision-language performance on CT enterography, with retrieval-augmented generation substantially improving automated report severity accuracy over fine-tuning alone.
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MIRAGE: Retrieval and Generation of Multimodal Images and Texts for Medical Education
MIRAGE combines a medical CLIP model, a diffusion generator, and an LLM into an accessible interface for retrieving and creating educational medical images and texts.