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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2505.20928 |
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| _version_ | 1866916761754927104 |
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| author | Jaus, Alexander Marinov, Zdravko Seibold, Constantin Reiß, Simon Kleesiek, Jens Stiefelhagen, Rainer |
| author_facet | Jaus, Alexander Marinov, Zdravko Seibold, Constantin Reiß, Simon Kleesiek, Jens Stiefelhagen, Rainer |
| contents | Improving label quality in medical image segmentation is costly, but its benefits remain unclear. We systematically evaluate its impact using multiple pseudo-labeled versions of CT datasets, generated by models like nnU-Net, TotalSegmentator, and MedSAM. Our results show that while higher-quality labels improve in-domain performance, gains remain unclear if below a small threshold. For pre-training, label quality has minimal impact, suggesting that models rather transfer general concepts than detailed annotations. These findings provide guidance on when improving label quality is worth the effort. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20928 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Good Enough: Is it Worth Improving your Label Quality? Jaus, Alexander Marinov, Zdravko Seibold, Constantin Reiß, Simon Kleesiek, Jens Stiefelhagen, Rainer Computer Vision and Pattern Recognition Improving label quality in medical image segmentation is costly, but its benefits remain unclear. We systematically evaluate its impact using multiple pseudo-labeled versions of CT datasets, generated by models like nnU-Net, TotalSegmentator, and MedSAM. Our results show that while higher-quality labels improve in-domain performance, gains remain unclear if below a small threshold. For pre-training, label quality has minimal impact, suggesting that models rather transfer general concepts than detailed annotations. These findings provide guidance on when improving label quality is worth the effort. |
| title | Good Enough: Is it Worth Improving your Label Quality? |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.20928 |