Brain tumor image generation using an aggregation of GAN models with style transfer

In the recent past, deep learning-based models have achieved tremendous success in computer vision-related tasks with the help of large-scale annotated datasets. An interesting application of deep learning is synthetic data generation, especially in the domain of medical image analysis. The need for such a task arises due to the scarcity of original data. Class imbalance is another reason for applying data augmentation techniques. Generative Adversarial Networks (GANs) are beneficial for synthetic image generation in various fields. However, stand-alone GANs may only fetch the localized features in the latent representation of an image, whereas combining different GANs might understand the distributed features. To this end, we have proposed AGGrGAN, an aggregation of three base GAN models-two variants of Deep Convolutional Generative Adversarial Network (DCGAN) and a Wasserstein GAN (WGAN) to generate synthetic MRI scans of brain tumors. Further, we have applied the style transfer technique to enhance the image resemblance. Our proposed model efficiently overcomes the limitation of data unavailability and can understand the information variance in multiple representations of the raw images. We have conducted all the experiments on the two publicly available datasets - the brain tumor dataset and the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 dataset. Results show that the proposed model can generate fine-quality images with maximum Structural Similarity Index Measure (SSIM) scores of 0.57 and 0.83 on the said two datasets.

Automated summary

This paper discusses the application of deep learning in synthetic medical image generation, proposing an aggregation model AGGrGAN that combines different GAN models to generate synthetic MRI scans of brain tumors, and applies style transfer technique to enhance image resemblance.