{"ID":2856437,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.11553","arxiv_id":"2510.11553","title":"How many samples to label for an application given a foundation model? Chest X-ray classification study","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.","short_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 necessar...","url_abs":"https://arxiv.org/abs/2510.11553","url_pdf":"https://arxiv.org/pdf/2510.11553v2","authors":"[\"Nikolay Nechaev\",\"Evgeniia Przhezdzetskaia\",\"Viktor Gombolevskiy\",\"Dmitry Umerenkov\",\"Dmitry Dylov\"]","published":"2025-10-13T15:53:55Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}