Clinical VLMs enable image-to-report retrieval far above chance (15-50x at N=100-10k), persisting beyond disease labels, with targeted DP on projection heads cutting Recall@1 by 61.8% and preserving AUROC.
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MS-DKC is a dataset knowledge card framework that maps image, morphology, supervision, context, and risk descriptors to design priors and failure modes, shown to produce dataset-specific model adaptations with improved metrics on DRIVE, ISIC2018, and ACDC.
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Cross-modal linkage risk in clinical vision-language models
Clinical VLMs enable image-to-report retrieval far above chance (15-50x at N=100-10k), persisting beyond disease labels, with targeted DP on projection heads cutting Recall@1 by 61.8% and preserving AUROC.
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MS-DKC: A Dataset Knowledge Card Framework for Designing and Adapting Medical Image Segmentation Models
MS-DKC is a dataset knowledge card framework that maps image, morphology, supervision, context, and risk descriptors to design priors and failure modes, shown to produce dataset-specific model adaptations with improved metrics on DRIVE, ISIC2018, and ACDC.