  AI models present a wide range of applications in the field of medicine.
However, achieving optimal performance requires access to extensive healthcare
data, which is often not readily available. Furthermore, the imperative to
preserve patient privacy restricts patient data sharing with third parties and
even within institutes. Recently, generative AI models have been gaining
traction for facilitating 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. Considering the importance of the
problem, it has received little attention in the medical imaging community. To
this end, we assess memorization in unconditional latent diffusion models. We
train 2D and 3D latent diffusion models on CT, MR, and X-ray datasets for
synthetic data generation. Afterwards, we detect the amount of training data
memorized utilizing our self-supervised approach and further investigate
various factors that can influence memorization. Our findings show a
surprisingly high degree of patient data memorization across all datasets, with
approximately 40.9% of patient data being memorized and 78.5% of synthetic
samples identified as patient data copies on average in our experiments.
Further analyses reveal that using augmentation strategies during training can
reduce memorization while over-training the models can enhance it. Although
increasing the dataset size does not reduce memorization and might even enhance
it, it does lower the probability of a synthetic sample being a patient data
copy. Collectively, our results emphasize the importance of carefully training
generative models on private medical imaging datasets, and examining the
synthetic data to ensure patient privacy before sharing it for medical research
and applications.
