Does an ensemble of GANs lead to better performance when training segmentation networks with synthetic images?
This addresses the challenge of limited annotated datasets in medical imaging by improving synthetic image training, though it is incremental as it builds on existing GAN methods.
The paper tackled the problem of training segmentation networks with synthetic images, which often underperform compared to real images, by using an ensemble of 20 GANs to generate synthetic data, resulting in a Dice score increase of 4.7% to 14.0% on real test images for specific classes.
Large annotated datasets are required to train segmentation networks. In medical imaging, it is often difficult, time consuming and expensive to create such datasets, and it may also be difficult to share these datasets with other researchers. Different AI models can today generate very realistic synthetic images, which can potentially be openly shared as they do not belong to specific persons. However, recent work has shown that using synthetic images for training deep networks often leads to worse performance compared to using real images. Here we demonstrate that using synthetic images and annotations from an ensemble of 20 GANs, instead of from a single GAN, increases the Dice score on real test images with 4.7 % to 14.0 % on specific classes.