Boosting Sclera Segmentation through Semi-supervised Learning with Fewer Labels
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912187546599424 |
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| author | Wang, Guanjun Wang, Lu Niu, Ning Yao, Qiaoyi Wang, Yixuan Ren, Sufen Chen, Shengchao |
| author_facet | Wang, Guanjun Wang, Lu Niu, Ning Yao, Qiaoyi Wang, Yixuan Ren, Sufen Chen, Shengchao |
| contents | Sclera segmentation is crucial for developing automatic eye-related medical computer-aided diagnostic systems, as well as for personal identification and verification, because the sclera contains distinct personal features. Deep learning-based sclera segmentation has achieved significant success compared to traditional methods that rely on hand-crafted features, primarily because it can autonomously extract critical output-related features without the need to consider potential physical constraints. However, achieving accurate sclera segmentation using these methods is challenging due to the scarcity of high-quality, fully labeled datasets, which depend on costly, labor-intensive medical acquisition and expertise. To address this challenge, this paper introduces a novel sclera segmentation framework that excels with limited labeled samples. Specifically, we employ a semi-supervised learning method that integrates domain-specific improvements and image-based spatial transformations to enhance segmentation performance. Additionally, we have developed a real-world eye diagnosis dataset to enrich the evaluation process. Extensive experiments on our dataset and two additional public datasets demonstrate the effectiveness and superiority of our proposed method, especially with significantly fewer labeled samples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07750 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Boosting Sclera Segmentation through Semi-supervised Learning with Fewer Labels Wang, Guanjun Wang, Lu Niu, Ning Yao, Qiaoyi Wang, Yixuan Ren, Sufen Chen, Shengchao Computer Vision and Pattern Recognition Sclera segmentation is crucial for developing automatic eye-related medical computer-aided diagnostic systems, as well as for personal identification and verification, because the sclera contains distinct personal features. Deep learning-based sclera segmentation has achieved significant success compared to traditional methods that rely on hand-crafted features, primarily because it can autonomously extract critical output-related features without the need to consider potential physical constraints. However, achieving accurate sclera segmentation using these methods is challenging due to the scarcity of high-quality, fully labeled datasets, which depend on costly, labor-intensive medical acquisition and expertise. To address this challenge, this paper introduces a novel sclera segmentation framework that excels with limited labeled samples. Specifically, we employ a semi-supervised learning method that integrates domain-specific improvements and image-based spatial transformations to enhance segmentation performance. Additionally, we have developed a real-world eye diagnosis dataset to enrich the evaluation process. Extensive experiments on our dataset and two additional public datasets demonstrate the effectiveness and superiority of our proposed method, especially with significantly fewer labeled samples. |
| title | Boosting Sclera Segmentation through Semi-supervised Learning with Fewer Labels |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.07750 |