ScaFE：基于大语言模型生成的临床特征程序实现数据高效的瘢痕分类
ScaFE: Data-Efficient Scar Classification with LLM-Generated Clinical Feature Programs
AI总结：
ScaFE将LLM临床知识转化为本地可执行的特征程序，结合轻量级随机森林，在瘢痕分类任务中实现数据高效、跨站点的高性能，且决策可审计，优于现有基线模型。
AI中文摘要：
从临床照片中对病理性瘢痕进行分类，需要区分瘢痕疙瘩与增生性瘢痕，却面临专家标注数据有限、不同医院间采集差异大的问题。端到端图像模型仍依赖大量数据，而将照片上传至托管的视觉语言模型（VLM）可能违反本地数据治理要求，且其决策难以复现和审计。本文提出ScaFE（瘢痕特征工程），将大语言模型（LLM）中的临床知识转化为确定性、可执行的特征程序，而非要求模型直接诊断图像。联网的LLM检索临床证据并合成程序，用于测量瘢痕的视觉可评估属性；候选程序在受限本地环境中执行，仅返回聚合验证统计量和特征级SHAP摘要用于迭代修复与优化，原始图像和患者级输出均保留在本地。随后，轻量级随机森林对得到的结构化表示进行分类。在来自三家医院的600张照片上采用留一医院评估，ScaFE实现81.0%的医院宏平均平衡准确率，比最强基线BiomedCLIP高出10.0个百分点；仅用10%的开发数据时，ScaFE仍保持72.0%的平衡准确率，领先幅度达11.8个百分点。迭代优化还将可执行程序比例从66.7%提升至95.0%，最终特征中有91.7%得到验证证据支持。这些结果表明，LLM的知识可通过本地、可审计的特征程序支持数据高效的跨站点医学图像分类，而非依赖VLM的直接决策。
英文摘要：
Classifying pathological scars from clinical photographs requires distinguishing keloids from hypertrophic scars despite limited expert-labeled data and substantial acquisition variation across hospitals. End-to-end image models remain data-dependent, whereas sending photographs to a hosted vision-language model (VLM) may conflict with local data-governance requirements and yields decisions that are difficult to reproduce and audit. We introduce ScaFE (Scar Feature Engineering), which transfers clinical knowledge from a large language model (LLM) into deterministic, executable feature programs instead of asking the model to diagnose images. A web-enabled LLM retrieves clinical evidence and synthesizes programs that measure visually assessable scar attributes. Candidate programs execute in a restricted local environment, and only aggregate validation statistics and feature-level SHAP summaries are returned for iterative repair and refinement; raw images and patient-level outputs remain local. A lightweight Random Forest then operates on the resulting structured representation. On 600 photographs from three hospitals under leave-one-site-out evaluation, ScaFE achieves 81.0% site-macro balanced accuracy, exceeding the strongest baseline, BiomedCLIP, by 10.0 percentage points. With only 10% of the development data, ScaFE retains 72.0% balanced accuracy and an 11.8-point lead. Iterative refinement also raises the executable-program rate from 66.7% to 95.0%, with verified evidence for 91.7% of the final features. These results show that LLM knowledge can support data-efficient, cross-site medical image classification through local and auditable feature programs rather than direct VLM decisions.
