Unconditional Latent Diffusion Models Memorize Patient Imaging Data: Implications for Openly Sharing Synthetic Data

• URL: http://arxiv.org/abs/2402.01054v2
• Date: Mon, 15 Jul 2024 09:22:45 GMT
• Title: Unconditional Latent Diffusion Models Memorize Patient Imaging Data: Implications for Openly Sharing Synthetic Data
• Authors: Salman Ul Hassan Dar, Marvin Seyfarth, Jannik Kahmann, Isabelle Ayx, Theano Papavassiliu, Stefan O. Schoenberg, Norbert Frey, Bettina Baeßler, Sebastian Foersch, Daniel Truhn, Jakob Nikolas Kather, Sandy Engelhardt,
• Abstract summary: generative AI models have been gaining traction for facilitating open-data sharing. These models generate patient data copies instead of novel synthetic samples. We train 2D and 3D latent diffusion models on CT, MR, and X-ray datasets for synthetic data generation.
• Score: 2.1375651880073834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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