Learning to Generate Synthetic Medical Image Data with Context-Conditional GANs文章

This paper presents a novel approach to generate synthetic medical image data using context-conditional generative adversarial networks (GANs). The proposed method combines a conditional variational autoencoder (CVAE) and a GAN to generate realistic images conditioned on specific context information, such as patient age, gender, and pathology. The CVAE is used to encode the input context information into a latent vector that is then passed to the GAN. The GAN then generates synthetic medical images based on the latent vector. Experiments conducted on chest X-ray images show that the proposed method can generate high-quality synthetic medical images with realistic anatomy and pathology. Furthermore, it is shown that the generated images are conditioned on the input context information. The results demonstrate that this approach has potential applications in generating data for medical image analysis tasks such as disease diagnosis and prognosis.