A novel retinal image generation model with the preservation of structural similarity and high resolution

Due to data scarcity and class imbalance in medical images, the training dataset seriously affects the classification accuracy of the model. We propose a retinal image generation model based on GAN (RetiGAN). A dual-scale discriminator is operated to train the network at two scales to improve the quality of generated images. RetiGAN can better retain the semantic information of the original images under the guidance of the content loss due to the embedding of the VGG network into RetiGAN to extract the high-level semantic information of the original and the generated images. Besides, in order to enhance the details of the generated image, RetiGAN is guided to generate the retinal images with clearer edges by feeding smoothed images to the discriminator and forcing it to distinguish the smoothed from the original ones. The qualitative and quantitative analysis verifies that the generated retinal images are similar to the original ones in structure rather than simple copies. In addition, ablation experiments exhibit that the model can improve the resolution of generated images with better visibility and clearer edges. In summary, RetiGAN is superior to other retinal image generation models in the aspects of the preservation of structural similarity and high resolution.