Unconditional Latent Diffusion Models Memorize Patient Imaging Data

Generative latent diffusion models hold a wide range of applications in the medical imaging domain. A noteworthy application is privacy-preserved open-data sharing by proposing synthetic data as surrogates of real patient data. Despite the promise, these models are susceptible to patient data memorization, where models generate patient data copies instead of novel synthetic samples. This undermines the whole purpose of preserving patient data and may even result in patient re-identification. Considering the importance of the problem, surprisingly it has received …

application applications cs.cv cs.lg data data sharing diffusion diffusion models domain eess.iv generate generative imaging latent diffusion models medical medical imaging novel open-data patient privacy synthetic synthetic data