MedSynth: Leveraging Generative Model for Healthcare Data Sharing

Sharing medical datasets among healthcare organizations is essential for advancing AI-assisted disease diagnostics and enhancing patient care. Employing techniques like data de-identification and data synthesis in medical data sharing, however, comes with inherent drawbacks that may lead to privacy leakage. Therefore, there is a pressing need for mechanisms that can effectively conceal sensitive information, ensuring a secure environment for data sharing. Dataset Condensation (DC) emerges as a solution, creating a reduced-scale synthetic dataset from a larger original dataset while maintaining comparable training outcomes. This approach offers advantages in terms of privacy and communication efficiency in the context of medical data sharing. Despite these benefits, traditional condensation methods encounter challenges, particularly with high-resolution medical datasets. To address these challenges, we present MedSynth, a novel dataset condensation scheme designed to efficiently condense the knowledge within extensive medical datasets into a generative model. This facilitates the sharing of the generative model across hospitals without the need to disclose raw data. By combining an attention-based generator with a vision transformer (ViT), MedSynth creates a generative model capable of producing a concise set of representative synthetic medical images, encapsulating the features of the original dataset. This generative model can then be shared with hospitals to optimize various downstream model training tasks. Extensive experimental results across medical datasets demonstrate that MedSynth outperforms state-of-the-art methods. Moreover, MedSynth successfully defends against state-of-the-art Membership Inference Attacks (MIA), highlighting its significant potential in preserving the privacy of medical data.