Infinite Brain MR Images: PGGAN-Based Data Augmentation for Tumor Detection
Neural Approaches to Dynamics of Signal Exchanges
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Han, C., Rundo, L., Araki, R., Furukawa, Y., Mauri, G., Nakayama, H., & Hayashi, H. Infinite Brain MR Images: PGGAN-Based Data Augmentation for Tumor Detection. Springer, Neural Approaches to Dynamics of Signal Exchanges. [Book chapter]. https://doi.org/10.1007/978-981-13-8950-4_27
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.
External DOI: https://doi.org/10.1007/978-981-13-8950-4_27
This record's DOI: https://doi.org/10.17863/CAM.72023