Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection
This addresses the data scarcity issue in medical imaging for improved computer-assisted diagnosis, though it appears incremental as it builds on existing GAN methods.
The paper tackled the problem of limited annotated medical images for brain tumor detection by synthesizing realistic brain MR images using Progressive Growing of GANs (PGGANs) to augment data, achieving promising performance improvements in tumor detection tasks.
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.