HiCA, a hierarchical contrastive fine-tuning method for large vision-language models, is claimed to achieve state-of-the-art few-shot medical image classification, but the paper lacks the experimental detail needed to verify this claim.
Learning transferable visual models from na tural language supervision,
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Efficient Few-Shot Medical Image Analysis via Hierarchical Contrastive Vision-Language Learning
HiCA, a hierarchical contrastive fine-tuning method for large vision-language models, is claimed to achieve state-of-the-art few-shot medical image classification, but the paper lacks the experimental detail needed to verify this claim.