Generation of retinal OCT images with diseases based on cGAN

Data imbalance is a classic problem in image classification, especially for medical images where noirnal data is much more than data with diseases. To make up for the absence of disease images, methods which can generate retinal OCT images with diseases from noimal retinal images are investigated. Conditional GANs (cGAN) have shown significant success in natural images generation, but the applications for medical images are limited. In this work, we propose an end-to-end framework for OCT image generation based on cGAN. The new structural similarity index (SSIM) loss is introduced so that the model can take the structure-related details into consideration. In experiments, three kinds of retinal disease images are generated. The generated images assume the natural structure of the retina and thus are visually appealing. The method is further validated by testing the classification perforr lance trained by the generated images.