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

Accepted version
Peer-reviewed

Type

Book chapter

Change log

Authors

Han, C 
Araki, R 
Furukawa, Y 
Mauri, G 

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 multistage generative training method, to generate original-sized 256 × 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 a promising performance improvement, when combined with classical DA, in tumor detection and also in other medical imaging tasks.

Description

Title

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

Keywords

46 Information and Computing Sciences, 4611 Machine Learning, Bioengineering, Biomedical Imaging, Cancer, Neurosciences, Brain Disorders, Neurological

Is Part Of

Neural Approaches to Dynamics of Signal Exchanges

Book type

Publisher

Springer

ISBN

978-981-13-8949-8