This paper addresses the emerging task of recognizing multiple retinal
diseases from wide-field (WF) and ultra-wide-field (UWF) fundus images. For an
effective reuse of existing labeled color fundus photo (CFP) data, we propose
Cross-domain Collaborative Learning (CdCL). Inspired by the success of
fixed-ratio based mixup in unsupervised domain adaptation, we re-purpose this
strategy for the current task. Due to the intrinsic disparity between the
field-of-view of CFP and WF/UWF images, a scale bias naturally exists in a
mixup sample that the anatomic structure from a CFP image will be considerably
larger than its WF/UWF counterpart. The CdCL method resolves the issue by
Scale-bias Correction, which employs Transformers for producing scale-invariant
features. As demonstrated by extensive experiments on multiple datasets
covering both WF and UWF images, the proposed method compares favorably against
a number of competitive baselines.

该研究提出了一种基于 Cross-domain Collaborative Learning 的多种视网膜疾病从广视野和超广视野眼底图像中识别的方法，并在多个数据集上与其它基线模型进行比较。