Medical variational autoencoder and generative adversarial network for medical imaging

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