ProtoCLIP: Prototype-Aligned Latent Refinement for Robust Zero-Shot Chest X-Ray Classification explores ProtoCLIP enhances zero-shot chest X-ray classification by refining vision-language models with curated data and prototype alignment.. Commercial viability score: 8/10 in Medical AI.
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Page Freshness
Canonical route: /paper/protoclip-prototype-aligned-latent-refinement-for-robust-zero-shot-chest-x-ray-classification
Page-specific freshness sourced from this paper's evidence receipt and score bundle.
Agent Handoff
Canonical ID protoclip-prototype-aligned-latent-refinement-for-robust-zero-shot-chest-x-ray-classification | Route /paper/protoclip-prototype-aligned-latent-refinement-for-robust-zero-shot-chest-x-ray-classification
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/protoclip-prototype-aligned-latent-refinement-for-robust-zero-shot-chest-x-ray-classificationMCP example
{
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"arguments": {
"arxiv_id": "2604.18444"
}
}source_context
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"route": "/paper/protoclip-prototype-aligned-latent-refinement-for-robust-zero-shot-chest-x-ray-classification",
"paper_ref": "protoclip-prototype-aligned-latent-refinement-for-robust-zero-shot-chest-x-ray-classification",
"topic_slug": null,
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"dataset_ref": null
}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
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