Heterogeneous medical data integration with Multi-Source StyleGAN

Conditional deep generative models have emerged as powerful tools for generating realistic images enabling fine-grained control over latent factors. In the medical domain, data scarcity and the need to integrate information from diverse sources present challenges for existing generative models, often resulting in low-quality image generation and poor controllability. To address these two issues, we propose Multi-Source StyleGAN (MSSG). MSSG learns jointly from multiple heterogeneous data sources with different available covariates and can generate new images controlling all covariates together, thereby overcoming both data scarcity and heterogeneity. We validate our method on semi-synthetic data of handwritten digit images with varying morphological features and in controlled multi-source simulations on retinal fundus images and brain magnetic resonance images. Finally, we apply MSSG in a real-world setting of brain MRI from different sources. Our proposed algorithm offers a promising direction for unbiased data generation from disparate sources. For the reproducibility of our experimental results, we provide detailed code implementation (1).