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Bibliographische Detailangaben
Hauptverfasser: 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
Format: Preprint
Veröffentlicht: 2025
Schlagworte:
Computer Vision and Pattern Recognition
Online-Zugang:https://arxiv.org/abs/2510.11073
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Inhaltsangabe:
  • 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.

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