  Generative latent diffusion models have been established as state-of-the-art
in data generation. One promising application is generation of realistic
synthetic medical imaging data for open data sharing without compromising
patient privacy. Despite the promise, the capacity of such models to memorize
sensitive patient training data and synthesize samples showing high resemblance
to training data samples is relatively unexplored. Here, we assess the
memorization capacity of 3D latent diffusion models on photon-counting coronary
computed tomography angiography and knee magnetic resonance imaging datasets.
To detect potential memorization of training samples, we utilize
self-supervised models based on contrastive learning. Our results suggest that
such latent diffusion models indeed memorize training data, and there is a dire
need for devising strategies to mitigate memorization.
