aiqbench analysis
This research demonstrates that standard de-identification protocols fail because deep learning models can reconstruct patient likenesses directly from medical scan pixels, effectively treating them as biometric data rather than anonymous records. The study utilizes cycle-consistent variational autoencoders to establish a correspondence between cross-sectional images and identifiable portraits, revealing significant vulnerabilities in current privacy assumptions for healthcare AI systems. Consequently, regulatory frameworks governing medical imaging must evolve to account for the inherent identifiability of image content regardless of metadata removal.
MirrorNet: Can Medical Image Anonymization Really Protect Patient Identity?
arXiv:2608.05938v1 Announce Type: cross Abstract: 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 recognisable likeness of the patient (identity-region MAE = 0.163); conversely, it synthesises a scan from such an image. These results indicate that a de-identified medical scan remains identifying---it is, in effect, a photograph of the patient---and that imaging data should be governed as biometric data rather than as anonymisable records. To support reproducibility, the code and trained models are shared at https://github.com/attilasimko/public-repository.
Title:MirrorNet: Can Medical Image Anonymization Really Protect Patient Identity?
Abstract: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 recognisable likeness of the patient (identity-region MAE = 0.163); conversely, it synthesises a scan from such an image. These results indicate that a de-identified medical scan remains identifying---it is, in effect, a photograph of the patient---and that imaging data should be governed as biometric data rather than as anonymisable records. To support reproducibility, the code and trained models are shared at this https URL.
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
arXiv:2608.05938 [cs.CV]
(or arXiv:2608.05938v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.05938 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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