  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, there is an urgent need to obfuscate the biometric
information appearing in the images. We propose the first deep learning-based
approach (PriCheXy-Net) to targetedly anonymize chest radiographs while
maintaining data utility for diagnostic and machine learning purposes. Our
model architecture is a composition of three independent neural networks that,
when collectively used, allow for learning a deformation field that is able to
impede patient re-identification. Quantitative results on the ChestX-ray14
dataset show a reduction of patient re-identification from 81.8% to 57.7% (AUC)
after re-training with little impact on the abnormality classification
performance. This indicates the ability to preserve underlying abnormality
patterns while increasing patient privacy. Lastly, we compare our proposed
anonymization approach with two other obfuscation-based methods (Privacy-Net,
DP-Pix) and demonstrate the superiority of our method towards resolving the
privacy-utility trade-off for chest radiographs.
