MirrorNet: Can Medical Image Anonymization Really Protect Patient Identity?

TL;DR - An arXiv cs.CV preprint showing that de-identified medical scans still carry patient identity in the pixels themselves, arguing such imaging data should be governed as biometric rather than anonymizable data.

• Uses a pair of coupled, cycle-consistent variational autoencoders to learn a bidirectional correspondence between cross-sectional medical images and non-medical, patient-identifying images.
• From a held-out scan the model reconstructs a recognisable likeness of the patient (identity-region MAE = 0.163), and can invert the mapping to synthesise a scan from an identifying photo.
• Implication: standard metadata stripping (names, dates) protects headers but not image content, so shared research/teaching/benchmark datasets may leak identity even without visible facial structures.
• Code and trained models are released for reproducibility at the authors' public GitHub repository.