RadImageGAN -- A Multi-modal Dataset-Scale Generative AI for Medical Imaging

Zelong Liu; Alexander Zhou; Arnold Yang; Alara Yilmaz; Maxwell Yoo; Mikey Sullivan; Catherine Zhang; James Grant; Daiqing Li; Zahi A. Fayad; Sean Huver; Timothy Deyer; Xueyan Mei

arXiv · 2312.05953

RadImageGAN -- A Multi-modal Dataset-Scale Generative AI for Medical Imaging

Zelong Liu·Alexander Zhou·Arnold Yang·Alara Yilmaz·Maxwell Yoo·Mikey Sullivan·Catherine Zhang·James Grant·Daiqing Li·Zahi A. Fayad·Sean Huver·Timothy Deyer·Xueyan Mei

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Abstract

Deep learning in medical imaging often requires large-scale, high-quality data or initiation with suitably pre-trained weights. However, medical datasets are limited by data availability, domain-specific knowledge, and privacy concerns, and the creation of large and diverse radiologic databases like RadImageNet is highly resource-intensive. To address these limitations, we introduce RadImageGAN, the first multi-modal radiologic data generator, which was developed by training StyleGAN-XL on the real RadImageNet dataset of 102,774 patients. RadImageGAN can generate high-resolution synthetic medical imaging datasets across 12 anatomical regions and 130 pathological classes in 3 modalities. Furthermore, we demonstrate that RadImageGAN generators can be utilized with BigDatasetGAN to generate multi-class pixel-wise annotated paired synthetic images and masks for diverse downstream segmentation tasks with minimal manual annotation. We showed that using synthetic auto-labeled data from RadImageGAN can significantly improve performance on four diverse downstream segmentation datasets by augmenting real training data and/or developing pre-trained weights for fine-tuning. This shows that RadImageGAN combined with BigDatasetGAN can improve model performance and address data scarcity while reducing the resources needed for annotations for segmentation tasks.

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Zelong Liu, Alexander Zhou, Arnold Yang, Alara Yilmaz, Maxwell Yoo, Mikey Sullivan, Catherine Zhang, James Grant, Daiqing Li, Zahi A. Fayad, Sean Huver, Timothy Deyer, Xueyan Mei. 2023-12-10. RadImageGAN -- A Multi-modal Dataset-Scale Generative AI for Medical Imaging. https://arxiv.org/abs/2312.05953

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