Data Augmentation For Medical MR Image Using Generative Adversarial Networks
This work addresses data scarcity in medical imaging for improved computer-assisted diagnosis, though it is incremental as it builds on existing GAN techniques.
The paper tackled the problem of limited brain tumor MR image datasets for deep learning by improving a GAN-based data augmentation method, resulting in realistic 256x256 images that outperformed other GAN methods in FID and MS-SSIM metrics.
Computer-assisted diagnosis (CAD) based on deep learning has become a crucial diagnostic technology in the medical industry, effectively improving diagnosis accuracy. However, the scarcity of brain tumor Magnetic Resonance (MR) image datasets causes the low performance of deep learning algorithms. The distribution of transformed images generated by traditional data augmentation (DA) intrinsically resembles the original ones, resulting in a limited performance in terms of generalization ability. This work improves Progressive Growing of GANs with a structural similarity loss function (PGGAN-SSIM) to solve image blurriness problems and model collapse. We also explore other GAN-based data augmentation to demonstrate the effectiveness of the proposed model. Our results show that PGGAN-SSIM successfully generates 256x256 realistic brain tumor MR images which fill the real image distribution uncovered by the original dataset. Furthermore, PGGAN-SSIM exceeds other GAN-based methods, achieving promising performance improvement in Frechet Inception Distance (FID) and Multi-scale Structural Similarity (MS-SSIM).