Investigating Data Replication in Medical Synthetic Image Generation with Diffusion Models

Recent advancements in diffusion models have greatly enhanced image generation quality, offering promise for addressing data scarcity in medical imaging, particularly for rare diseases. However, diffusion models sometimes replicate training images, raising privacy concerns, especially in healthcare. This study investigates image replication in medical diffusion models, its frequency, and potential risks to patient privacy. We analyze types of replication in synthetic data and propose methods to detect and measure replication. To safeguard privacy, we introduce mitigation strategies that can be applied before releasing synthetic data. Finally, we assess the impact of replicated and non-replicated synthetic data on medical image classification tasks for X-ray, Ultrasound, and CT images following our proposed mitigation measures.