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arXiv · 2510.11553

How many samples to label for an application given a foundation model? Chest X-ray classification study

Nikolay Nechaev·Evgeniia Przhezdzetskaia·Viktor Gombolevskiy·Dmitry Umerenkov·Dmitry Dylov
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Abstract

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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Nikolay Nechaev, Evgeniia Przhezdzetskaia, Viktor Gombolevskiy, Dmitry Umerenkov, Dmitry Dylov. 2025-10-13. How many samples to label for an application given a foundation model? Chest X-ray classification study. https://arxiv.org/abs/2510.11553

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