Medical variational autoencoder and generative adversarial network for medical imaging

Abstract

Generative adversarial networks have succeeded promising results in the medical imaging field. One of the most significant challenges in this regard is the lack of or limited data sharing. In our work, an approach for combining generative adversarial network (GAN) and variational autoencoder (VAE) models has been proposed to improve the accuracy and efficiency of medical image analysis tasks. Our approach leverages the capacity of VAEs to acquire condensed feature representations, and the ability of GANs to generate high-quality synthetic images to learn an embedding that keeps high-level abstract visual qualities. Inception score (IS) and Fréchet inception distance (FID) score have been generated in order to demonstrate the high quality of images. Based on the score results, our approach demonstrates the potential of VAE-GAN fusion models and clearly outperforms existing methods on a variety of medical image analysis tasks. The suggested algorithm is explained, as are the results and evaluations.