Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection

TL;DR

This paper introduces a PGGAN-based data augmentation method to generate realistic high-resolution brain MR images, significantly enhancing tumor detection accuracy by providing diverse training data beyond traditional augmentation techniques.

Contribution

The study presents a novel application of Progressive Growing of GANs for synthesizing high-resolution brain MR images to improve tumor detection performance.

Findings

PGGAN-based augmentation improves tumor detection accuracy.

Synthesized images are realistic and diverse, aiding training.

Combining PGGAN with classical augmentation yields better results.

Abstract

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.