mlsanityData to 7 Oct 2026

ROFI: A Deep Learning-Based Ophthalmic Sign-Preserving and Reversible Patient Face Anonymizer

arXiv 2510.11073 ↗First posted 13 Oct 2025cs.CVcited by 1

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Abstract (arXiv)

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, kappa > 0.90). It achieves 100% diagnostic sensitivity and high agreement (kappa > 0.90) across eleven eye diseases in three cohorts, anonymizing over 95% of images. ROFI works with AI systems, maintaining original diagnoses (kappa > 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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 51 of its 158 references that are in the map. First posted on arXiv 13 Oct 2025.

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51 of 158 references.

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