Insufficient training data is a major bottleneck for most deep learning practices, not least in medical imaging where data is difficult to collect and publicly available datasets are scarce due to ethics and privacy. This work investigates the use of synthetic images, created by generative adversarial networks (GANs), as the only source of training data. We demonstrate that for this application, it is of great importance to make use of multiple GANs to improve the diversity of the generated data, i.e. to sufficiently cover the data distribution. While a single GAN can generate seemingly diverse image content, training on this data in most cases lead to severe over-fitting. We test the impact of ensembled GANs on synthetic 2D data as well as common image datasets (SVHN and CIFAR-10), and using both DCGANs and progressively growing GANs. As a specific use case, we focus on synthesizing digital pathology patches to provide anonymized training data.
@article{arxiv.2104.11797,
title = {Ensembles of GANs for synthetic training data generation},
author = {Gabriel Eilertsen and Apostolia Tsirikoglou and Claes Lundström and Jonas Unger},
journal= {arXiv preprint arXiv:2104.11797},
year = {2021}
}
Comments
ICLR 2021 workshop on Synthetic Data Generation: Quality, Privacy, Bias