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

By Changhee Han, Leonardo Rundo, Ryosuke Araki, Yujiro Furukawa, Giancarlo Mauri, Hideki Nakayama, Hideaki Hayashi. 2019

100 citations recorded by the source corpus.

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 \(256\times 256\) 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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology

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Citations in the corpus

GAN Memory with No Forgetting (2020, background): “…consequently also protecting privacy (the original data need not be saved); and ( iii ) a GAN often generates realistic samples not observed in training data, delivering a synthetic data augmentation that potentially beneﬁts better performance of downstream tasks [78, 8, 9, 21, 33, 26, 27].”
On Leveraging Pretrained GANs for Limited-Data Generation (2020, methodology): “…data augmentation like shift, zooming, rotation, or flipping, GANs trained on limited data can be used to do synthetic augmentation like transformed-style or fake observation-label/segmentation pair (Wang & Perez, 2017; Bowles et al., 2018; Frid-Adar et al., 2018; Han et al., 2019; 2020).”
Synthetic Data for Deep Learning (2019, background): “[229, 230] concentrate on brain magnetic resonance (MR) images.”
On Leveraging Pretrained GANs for Generation with Limited Data (2020, methodology): “…zooming, rotation, or flipping, GANs trained on limited data can be leveraged for synthetic augmentations, like synthesized observations with transformed styles (Wang & Perez, 2017) or fake observation-label/segmentation pairs (Bowles et al., 2018; Frid-Adar et al., 2018; Han et al., 2019; 2020).”
Brain tumor detection and classification using machine learning: a comprehensive survey (2021, background): “Accurate segmentation is a difficult task [265], as brain tumors have tentacles and diffused structures [43, 193, 220, 266].”