# Feeding the zombies: Synthesizing brain volumes using a 3D progressive growing GAN

arXiv: https://arxiv.org/abs/1912.05357 · HTML: https://mlsanity.com/paper/1912.05357/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [Advancing the AmbientGAN for learning stochastic object models](https://mlsanity.com/paper/2102.00281/) (2021)
- [Does an ensemble of GANs lead to better performance when training segmentation networks with synthetic images?](https://mlsanity.com/paper/2211.04086/) (2022)
- [GAN-GA: A Generative Model based on Genetic Algorithm for Medical Image Generation](https://mlsanity.com/paper/2401.00314/) (2023)

## Abstract (arXiv)

Deep learning requires large datasets for training (convolutional) networks with millions of parameters. In neuroimaging, there are few open datasets with more than 100 subjects, which makes it difficult to, for example, train a classifier to discriminate controls from diseased persons. Generative adversarial networks (GANs) can be used to synthesize data, but virtually all research is focused on 2D images. In medical imaging, and especially in neuroimaging, most datasets are 3D or 4D. Here we therefore present preliminary results showing that a 3D progressive growing GAN can be used to synthesize MR brain volumes.