MirrorNet: Can Medical Image Anonymization Really Protect Patient Identity?
Medical images are routinely de-identified---names, dates, and other metadata removed---and then shared for research, teaching, and public benchmarks under the assumption that this renders them anonymous. Such de-identification protects the metadata but not the pixels, and---apart from scans that directly contain facial structures---whether the image content itself identifies the patient has received little scrutiny. We investigate this question by learning a cycle-consistent correspondence between a cross-sectional medical image and a non-medical, patient-identifying image, using a pair of coupled, cycle-consistent variational autoencoders. From a held-out scan, the model recovers a reco...
| Authors | Attila Simkó |
|---|---|
| Primary category | cs.CV |
| Published | 6 August 2026 |
| Last updated at source | 6 August 2026 |
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