ROFI: A Deep Learning-Based Ophthalmic Sign-Preserving and Reversible Patient Face Anonymizer
Abstrak
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.
Topik & Kata Kunci
Penulis (22)
Yuan Tian
Min Zhou
Yitong Chen
Fang Li
Lingzi Qi
Shuo Wang
Xieyang Xu
Yu Yu
Shiqiong Xu
Chaoyu Lei
Yankai Jiang
Rongzhao Zhang
Jia Tan
Li Wu
Hong Chen
Xiaowei Liu
Wei Lu
Lin Li
Huifang Zhou
Xuefei Song
Guangtao Zhai
Xianqun Fan
Akses Cepat
- Tahun Terbit
- 2025
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓