Ultra-wide-field Fundus Image Synthesis Using Various GAN Models

Abstract

Many people lose sight due to diabetic retinopathy. The reason that diabetic retinopathy is dangerous is that it cannot return to its pre-onset state after the disease's onset. Most patients take fundus images that capture the retina, and the doctor uses the fundus images to determine the presence of disease. Existing fundus images could only identify a narrow range, making it difficult to diagnose the disease accurately. However, with technological advances, ultra-wide-field fundus images that allow the wider retina to be seen have emerged. However, in deep learning research, many studies use existing fundus images due to the lack of new data. In the case of new technologies such as ultra-wide-field fundus images, it was often difficult to obtain data, so deep learning research could not be done properly. In the case of ultra-wide-field fundus images, research was conducted using data from hundreds to ten thousand sheets, but compared to large-scale data sets, the deep learning performance is inevitably inferior compared to large-scale data sets. In this study, synthetic data were created using ultra-wide-field fundus images and various GAN models to solve this problem. As a result of the study, BEGAN was derived similarly to the real image in qualitative and quantitative evaluation. However, it fell into mode collapse and showed the same output even when a new input came in. Mode collapse in BEGAN could be appeared depending on the amount and size of data, so various studies using BEGAN are needed.