Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing
This addresses privacy concerns and data sharing limitations in medical analytics, particularly for brain tumor imaging, with incremental improvements in segmentation performance.
The study tackled the problem of privacy and data heterogeneity in medical imaging by proposing a decentralized few-shot generative model (DFGM) to synthesize brain tumor images, achieving Dice score improvements of 3.9% for data augmentation and 4.6% for fairness.
Leveraging multi-center data for medical analytics presents challenges due to privacy concerns and data heterogeneity. While distributed approaches such as federated learning has gained traction, they remain vulnerable to privacy breaches, particularly in sensitive domains like medical imaging. Generative models, such as diffusion models, enhance privacy by synthesizing realistic data. However, they are prone to memorization, especially when trained on small datasets. This study proposes a decentralized few-shot generative model (DFGM) to synthesize brain tumor images while fully preserving privacy. DFGM harmonizes private tumor data with publicly shareable healthy images from multiple medical centers, constructing a new dataset by blending tumor foregrounds with healthy backgrounds. This approach ensures stringent privacy protection and enables controllable, high-quality synthesis by preserving both the healthy backgrounds and tumor foregrounds. We assess DFGM's effectiveness in brain tumor segmentation using a UNet, achieving Dice score improvements of 3.9% for data augmentation and 4.6% for fairness on a separate dataset.