Due to the lack of available annotated medical images, accurate computer-assisted diagnosis requires intensive Data Augmentation (DA) techniques, such as geometric/intensity transformations of original images; however, those transformed images intrinsically have a similar distribution to the original ones, leading to limited performance improvement. To fill the data lack in the real image distribution, we synthesize brain contrast-enhanced Magnetic Resonance (MR) images---realistic but completely different from the original ones---using Generative Adversarial Networks (GANs). This study exploits Progressive Growing of GANs (PGGANs), a multi-stage generative training method, to generate original-sized 256 X 256 MR images for Convolutional Neural Network-based brain tumor detection, which is challenging via conventional GANs; difficulties arise due to unstable GAN training with high resolution and a variety of tumors in size, location, shape, and contrast. Our preliminary results show that this novel PGGAN-based DA method can achieve promising performance improvement, when combined with classical DA, in tumor detection and also in other medical imaging tasks.
@article{arxiv.1903.12564,
title = {Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection},
author = {Changhee Han and Leonardo Rundo and Ryosuke Araki and Yujiro Furukawa and Giancarlo Mauri and Hideki Nakayama and Hideaki Hayashi},
journal= {arXiv preprint arXiv:1903.12564},
year = {2019}
}
Comments
13 pages, 6 figures, Accepted to Neural Approaches to Dynamics of Signal Exchanges as a Springer book chapter