End-To-End Retina Image Synthesis Based on CGAN Using Class Feature Loss and Improved Retinal Detail Loss

Abstract

Retinal images are the most direct and effective basis for Diabetic Retinopathy (DR) diagnosis. With the rapid development of deep learning, the technology of retinal image-assisted diagnosis based on deep learning is widely used in the field of DR intelligent diagnosis. However, the training of deep neural network usually requires a large number of annotated samples, but retinal images annotated by professional doctors are cost-expensive and difficult to obtain, which limits the application of deep learning technology in DR intelligent diagnosis. In order to alleviate the scarcity of labelled retinal images, we propose an end-to-end conditional generative adversarial network with class feature loss and improved retinal detail loss. The network combines the above two losses with the adversarial loss, and jointly constrains the generator to generate high-quality retinal images. The proposed retinal detail loss is summed over physiological detail loss which is meant to preserve high-level semantic features of the physiological details contained in the fundus images and pixel loss which ensures the low-level features in synthesized image will not deviate from the real image. In addition, the class feature loss constrains the synthesized images to be consistent with the real images in class features representation, which further makes the synthesized images have pathological features of the corresponding grade. The generated images by the proposed network are evaluated from three objective metrics including the subjective effect and the FID, SWD, which are used to evaluate the quality and diversity of generated images, and the effect of retinal vessel segmentation, respectively. Experimental results demonstrate that our synthesized images have superior performance on both the quality and quantity.

Keywords

• Diabetic retinopathy
• retinal image synthesis
• conditional generative adversarial network
• deep learning
• DR~grading