Large-Scale Label Quality Assessment for Medical Segmentation via a Vision-Language Judge and Synthetic Data
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915742519132160 |
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| author | Chen, Yixiong Zhou, Zongwei Li, Wenxuan Yuille, Alan |
| author_facet | Chen, Yixiong Zhou, Zongwei Li, Wenxuan Yuille, Alan |
| contents | Large-scale medical segmentation datasets often combine manual and pseudo-labels of uneven quality, which can compromise training and evaluation. Low-quality labels may hamper performance and make the model training less robust. To address this issue, we propose SegAE (Segmentation Assessment Engine), a lightweight vision-language model (VLM) that automatically predicts label quality across 142 anatomical structures. Trained on over four million image-label pairs with quality scores, SegAE achieves a high correlation coefficient of 0.902 with ground-truth Dice similarity and evaluates a 3D mask in 0.06s. SegAE shows several practical benefits: (I) Our analysis reveals widespread low-quality labeling across public datasets; (II) SegAE improves data efficiency and training performance in active and semi-supervised learning, reducing dataset annotation cost by one-third and quality-checking time by 70% per label. This tool provides a simple and effective solution for quality control in large-scale medical segmentation datasets. The dataset, model weights, and codes are released at https://github.com/Schuture/SegAE. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_14406 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Large-Scale Label Quality Assessment for Medical Segmentation via a Vision-Language Judge and Synthetic Data Chen, Yixiong Zhou, Zongwei Li, Wenxuan Yuille, Alan Computer Vision and Pattern Recognition Image and Video Processing Large-scale medical segmentation datasets often combine manual and pseudo-labels of uneven quality, which can compromise training and evaluation. Low-quality labels may hamper performance and make the model training less robust. To address this issue, we propose SegAE (Segmentation Assessment Engine), a lightweight vision-language model (VLM) that automatically predicts label quality across 142 anatomical structures. Trained on over four million image-label pairs with quality scores, SegAE achieves a high correlation coefficient of 0.902 with ground-truth Dice similarity and evaluates a 3D mask in 0.06s. SegAE shows several practical benefits: (I) Our analysis reveals widespread low-quality labeling across public datasets; (II) SegAE improves data efficiency and training performance in active and semi-supervised learning, reducing dataset annotation cost by one-third and quality-checking time by 70% per label. This tool provides a simple and effective solution for quality control in large-scale medical segmentation datasets. The dataset, model weights, and codes are released at https://github.com/Schuture/SegAE. |
| title | Large-Scale Label Quality Assessment for Medical Segmentation via a Vision-Language Judge and Synthetic Data |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2601.14406 |