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For decades, advances in retinal imaging technology have enabled effectivediagnosis and management of retinal disease using fundus cameras. Recently,ultra-wide-field (UWF) fundus imaging by Optos camera is gradually put into usebecause of its broader insights on fundus for some lesions that are nottypically seen in traditional fundus images. Research on traditional fundusimages is an active topic but studies on UWF fundus images are few. One of themost important reasons is that UWF fundus images are hard to obtain. In thispaper, for the first time, we explore domain adaptation from the traditionalfundus to UWF fundus images. We propose a flexible framework to bridge thedomain gap between two domains and co-train a UWF fundus diagnosis model bypseudo-labelling and adversarial learning. We design a regularisation techniqueto regulate the domain adaptation. Also, we apply MixUp to overcome theover-fitting issue from incorrect generated pseudo-labels. Our experimentalresults on either single or both domains demonstrate that the proposed methodcan well adapt and transfer the knowledge from traditional fundus images to UWFfundus images and improve the performance of retinal disease recognition.

Bridge the Domain Gap Between Ultra-wide-field and Traditional Fundus Images via Adversarial Domain Adaptation