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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/2510.11073 |
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| _version_ | 1866909841066295296 |
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| author | Tian, Yuan Zhou, Min Chen, Yitong Li, Fang Qi, Lingzi Wang, Shuo Xu, Xieyang Yu, Yu Xu, Shiqiong Lei, Chaoyu Jiang, Yankai Zhang, Rongzhao Tan, Jia Wu, Li Chen, Hong Liu, Xiaowei Lu, Wei Li, Lin Zhou, Huifang Song, Xuefei Zhai, Guangtao Fan, Xianqun |
| author_facet | Tian, Yuan Zhou, Min Chen, Yitong Li, Fang Qi, Lingzi Wang, Shuo Xu, Xieyang Yu, Yu Xu, Shiqiong Lei, Chaoyu Jiang, Yankai Zhang, Rongzhao Tan, Jia Wu, Li Chen, Hong Liu, Xiaowei Lu, Wei Li, Lin Zhou, Huifang Song, Xuefei Zhai, Guangtao Fan, Xianqun |
| contents | Patient face images provide a convenient mean for evaluating eye diseases, while also raising privacy concerns. Here, we introduce ROFI, a deep learning-based privacy protection framework for ophthalmology. Using weakly supervised learning and neural identity translation, ROFI anonymizes facial features while retaining disease features (over 98\% accuracy, $κ> 0.90$). It achieves 100\% diagnostic sensitivity and high agreement ($κ> 0.90$) across eleven eye diseases in three cohorts, anonymizing over 95\% of images. ROFI works with AI systems, maintaining original diagnoses ($κ> 0.80$), and supports secure image reversal (over 98\% similarity), enabling audits and long-term care. These results show ROFI's effectiveness of protecting patient privacy in the digital medicine era. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11073 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ROFI: A Deep Learning-Based Ophthalmic Sign-Preserving and Reversible Patient Face Anonymizer Tian, Yuan Zhou, Min Chen, Yitong Li, Fang Qi, Lingzi Wang, Shuo Xu, Xieyang Yu, Yu Xu, Shiqiong Lei, Chaoyu Jiang, Yankai Zhang, Rongzhao Tan, Jia Wu, Li Chen, Hong Liu, Xiaowei Lu, Wei Li, Lin Zhou, Huifang Song, Xuefei Zhai, Guangtao Fan, Xianqun Computer Vision and Pattern Recognition Patient face images provide a convenient mean for evaluating eye diseases, while also raising privacy concerns. Here, we introduce ROFI, a deep learning-based privacy protection framework for ophthalmology. Using weakly supervised learning and neural identity translation, ROFI anonymizes facial features while retaining disease features (over 98\% accuracy, $κ> 0.90$). It achieves 100\% diagnostic sensitivity and high agreement ($κ> 0.90$) across eleven eye diseases in three cohorts, anonymizing over 95\% of images. ROFI works with AI systems, maintaining original diagnoses ($κ> 0.80$), and supports secure image reversal (over 98\% similarity), enabling audits and long-term care. These results show ROFI's effectiveness of protecting patient privacy in the digital medicine era. |
| title | ROFI: A Deep Learning-Based Ophthalmic Sign-Preserving and Reversible Patient Face Anonymizer |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.11073 |