  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 use of existing large amount of labeled color fundus photo (CFP) data
and the relatively small amount of WF and UWF data, we propose a supervised
domain adaptation method named 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.
