用少量标注样本即可实现胸片分类高精度，降低人工标注成本。
How many samples to label for an application given a foundation model? Chest X-ray classification study
- 通过幂律拟合预测达到目标性能所需的最少标注样本数。
- XrayCLIP和XraySigLIP仅需50个样本即接近性能极限。
- 只需50个样本的学习曲线就能准确预判最终模型表现。
胸片分类对精准诊断至关重要，但传统方法依赖大量标注数据，成本高昂。基础模型虽可缓解此问题，但具体需要多少标注样本仍不明确。本文系统评估了幂律拟合在预测达到特定ROC-AUC阈值所需训练规模中的应用。在多种病灶类型和基础模型上测试发现，XrayCLIP与XraySigLIP在远少于ResNet-50基线的情况下即实现优异性能。关键发现：仅用50个标注样本学习曲线的斜率，即可准确预测最终性能饱和点。研究结果帮助从业者仅标注必要样本，显著降低标注成本。
原文摘要 · Abstract (English)
Chest X-ray classification is vital yet resource-intensive, typically demanding extensive annotated data for accurate diagnosis. Foundation models mitigate this reliance, but how many labeled samples are required remains unclear. We systematically evaluate the use of power-law fits to predict the training size necessary for specific ROC-AUC thresholds. Testing multiple pathologies and foundation models, we find XrayCLIP and XraySigLIP achieve strong performance with significantly fewer labeled examples than a ResNet-50 baseline. Importantly, learning curve slopes from just 50 labeled cases accurately forecast final performance plateaus. Our results enable practitioners to minimize annotation costs by labeling only the essential samples for targeted performance.
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