ScaFE: Data-Efficient Scar Classification with LLM-Generated Clinical Feature Programs
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75
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90
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39
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Merged summary
TL;DR - ScaFE uses an LLM to generate deterministic clinical feature programs for local scar-image classification, improving auditability and data governance. It outperforms BiomedCLIP across hospitals while remaining effective with limited labeled data.
- Achieves 81.0% site-macro balanced accuracy on 600 images from three hospitals, 10 points above BiomedCLIP.
- Retains 72.0% accuracy with 10% of development data, maintaining an 11.8-point lead.
- Keeps raw images and patient-level outputs local; only aggregate metrics and SHAP summaries support LLM-guided refinement.
- Iteration increases executable programs from 66.7% to 95.0%, with verified evidence for 91.7% of final features.
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ScaFE: Data-Efficient Scar Classification with LLM-Generated Clinical Feature Programs
Public signals
Semantic Scholar citations 0 · Semantic Scholar influential citations 0
TL;DR - ScaFE uses an LLM to generate deterministic clinical feature programs for local scar-image classification, improving auditability and data governance. It outperforms BiomedCLIP across hospitals while remaining effective with limited labeled data.
- Achieves 81.0% site-macro balanced accuracy on 600 images from three hospitals, 10 points above BiomedCLIP.
- Retains 72.0% accuracy with 10% of development data, maintaining an 11.8-point lead.
- Keeps raw images and patient-level outputs local; only aggregate metrics and SHAP summaries support LLM-guided refinement.
- Iteration increases executable programs from 66.7% to 95.0%, with verified evidence for 91.7% of final features.