Deep Learning-based Anonymization of Chest Radiographs: A Utility-preserving Measure for Patient Privacy. (arXiv:2209.11531v1 [eess.IV])

Robust and reliable anonymization of chest radiographs constitutes an
essential step before publishing large datasets of such for research purposes.
The conventional anonymization process is carried out by obscuring personal
information in the images with black boxes and removing or replacing
meta-information. However, such simple measures retain biometric information in
the chest radiographs, allowing patients to be re-identified by a linkage
attack. Therefore, we see an urgent need to obfuscate the biometric information
appearing in the images. To the best …

deep learning patient privacy privacy utility