  For decades, advances in retinal imaging technology have enabled effective
diagnosis and management of retinal disease using fundus cameras. Recently,
ultra-wide-field (UWF) fundus imaging by Optos camera is gradually put into use
because of its broader insights on fundus for some lesions that are not
typically seen in traditional fundus images. Research on traditional fundus
images is an active topic but studies on UWF fundus images are few. One of the
most important reasons is that UWF fundus images are hard to obtain. In this
paper, for the first time, we explore domain adaptation from the traditional
fundus to UWF fundus images. We propose a flexible framework to bridge the
domain gap between two domains and co-train a UWF fundus diagnosis model by
pseudo-labelling and adversarial learning. We design a regularisation technique
to regulate the domain adaptation. Also, we apply MixUp to overcome the
over-fitting issue from incorrect generated pseudo-labels. Our experimental
results on either single or both domains demonstrate that the proposed method
can well adapt and transfer the knowledge from traditional fundus images to UWF
fundus images and improve the performance of retinal disease recognition.

Image and Video Processing;Computer Vision and Pattern Recognition