mlsanityData to 7 Oct 2026

Does an ensemble of GANs lead to better performance when training segmentation networks with synthetic images?

arXiv 2211.04086 ↗First posted 8 Nov 2022cs.CV eess.IVcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 23 references that are in the map. First posted on arXiv 8 Nov 2022.

Builds on

15 of 23 references.

References · 15

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3 more
  1. 2020Synthesizing brain tumor images and annotations by combining progressive growing GAN and SPADEcited by 9
  2. 2021Ensembles of GANs for synthetic training data generationcited by 6
  3. 2019Feeding the zombies: Synthesizing brain volumes using a 3D progressive growing GANcited by 3

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Citing papers per year: 2023: 2.

2 papers.

  1. 2023Beware of diffusion models for synthesizing medical images -- A comparison with GANs in terms of memorizing brain MRI and chest x-ray imagescited by 23
  2. 2023Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelscited by 19