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| Item Type: | Conference or Workshop Item |
|---|---|
| Title: | Heterogeneous medical data integration with Multi-Source StyleGAN |
| Creators Name: | Lai, W.C., Kirchler, M., Yassin, H., Fehr, J., Rakowski, A., Olsson, H., Starke, L., Millward, J.M., Waiczies, S. and Lippert, C. |
| Abstract: | 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). |
| Keywords: | Generative Models, StyleGAN, Multi-Source, MRI, Retinal Fundus Images |
| Source: | Proceedings of Machine Learning Research |
| Series Name: | Proceedings of Machine Learning Research |
| Title of Book: | Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning |
| ISSN: | 2640-3498 |
| Publisher: | PMLR |
| Volume: | 250 |
| Page Range: | 857-887 |
| Date: | 2024 |
| External Fulltext: | View full text on external repository or document server |
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