  Recently, ultra-widefield (UWF) 200\degree~fundus imaging by Optos cameras
has gradually been introduced because of its broader insights for detecting
more information on the fundus than regular 30 degree - 60 degree fundus
cameras. Compared with UWF fundus images, regular fundus images contain a large
amount of high-quality and well-annotated data. Due to the domain gap, models
trained by regular fundus images to recognize UWF fundus images perform poorly.
Hence, given that annotating medical data is labor intensive and time
consuming, in this paper, we explore how to leverage regular fundus images to
improve the limited UWF fundus data and annotations for more efficient
training. We propose the use of a modified cycle generative adversarial network
(CycleGAN) model to bridge the gap between regular and UWF fundus and generate
additional UWF fundus images for training. A consistency regularization term is
proposed in the loss of the GAN to improve and regulate the quality of the
generated data. Our method does not require that images from the two domains be
paired or even that the semantic labels be the same, which provides great
convenience for data collection. Furthermore, we show that our method is robust
to noise and errors introduced by the generated unlabeled data with the
pseudo-labeling technique. We evaluated the effectiveness of our methods on
several common fundus diseases and tasks, such as diabetic retinopathy (DR)
classification, lesion detection and tessellated fundus segmentation. The
experimental results demonstrate that our proposed method simultaneously
achieves superior generalizability of the learned representations and
performance improvements in multiple tasks.
