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Found 2 papers, 1 papers with code
An Empirical Study of Aegis
We also compare the use of data augmentation to the robustness training of Aegis, and how Aegis performs under other adversarial attacks, such as the generation of adversarial examples.
Using Diffusion Models to Generate Synthetic Labelled Data for Medical Image Segmentation
Improvements over GAN methods were seen on average when the segmenter was entirely trained (DL difference: $-0. 0880 \pm 0. 0170$, IoU difference: $0. 0993 \pm 0. 01493$) or augmented (DL difference: GAN $-0. 1140 \pm 0. 0900 \text{ vs SD }-0. 1053 \pm 0. 0981$, IoU difference: GAN $0. 01533 \pm 0. 03831 \text{ vs SD }0. 0255 \pm 0. 0454$) with synthetic data.
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