Privacy-enhancing Image Sampling for the Synthesis of High-quality Anonymous Chest Radiographs

The development of well-performing deep learning-based algorithms for thoracic abnormality detection and classification relies on access to largescale chest X-ray datasets. However, the presence of patient-specific biometric information in chest radiographs impedes direct and public sharing of such data for research purposes due to the potential risk of patient re-identification. In this context, synthetic data generation emerges as a solution for anonymizing medical images. In this study, we utilize a privacy-enhancing sampling strategy within a latent diffusion model to generate fully anonymous chest radiographs.We conduct a comprehensive analysis of the employed method and examine the impact of different privacy degrees. For each configuration, the resulting synthetic images exhibit a substantial level of data utility, with only a marginal gap compared to real data. Qualitatively, a Turing test conducted with six radiologists confirms the high and realistic appearance of the generated chest radiographs, achieving an average classification accuracy of 55% across 50 images (25 real, 25 synthetic).