How many samples to label for an application given a foundation model? Chest X-ray classification study
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914107435778048 |
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| author | Nechaev, Nikolay Przhezdzetskaia, Evgeniia Gombolevskiy, Viktor Umerenkov, Dmitry Dylov, Dmitry |
| author_facet | Nechaev, Nikolay Przhezdzetskaia, Evgeniia Gombolevskiy, Viktor Umerenkov, Dmitry Dylov, Dmitry |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11553 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How many samples to label for an application given a foundation model? Chest X-ray classification study Nechaev, Nikolay Przhezdzetskaia, Evgeniia Gombolevskiy, Viktor Umerenkov, Dmitry Dylov, Dmitry Computer Vision and Pattern Recognition 68T07 (Primary) 68T45, 62H30, 62P10 (Secondary) 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. |
| title | How many samples to label for an application given a foundation model? Chest X-ray classification study |
| topic | Computer Vision and Pattern Recognition 68T07 (Primary) 68T45, 62H30, 62P10 (Secondary) |
| url | https://arxiv.org/abs/2510.11553 |