Five modality-specific CLIP-like medical models are aligned through a shared, distilled text embedding space, enabling zero-shot cross-modal retrieval and improved few-shot classification without paired image data.
As- sessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights into imaging , 3:573–589,
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Multimodal Medical Image Binding via Shared Text Embeddings
Five modality-specific CLIP-like medical models are aligned through a shared, distilled text embedding space, enabling zero-shot cross-modal retrieval and improved few-shot classification without paired image data.