Supervised Domain Adaptation for Recognizing Retinal Diseases from Wide-Field Fundus Images
This work addresses the challenge of leveraging existing labeled color fundus photo data for retinal disease recognition in wide-field images, which is an incremental improvement for medical imaging applications.
The paper tackles the problem of recognizing retinal diseases from wide-field and ultra-wide-field fundus images by proposing a supervised domain adaptation method called Cross-domain Collaborative Learning (CdCL), which uses scale-bias correction with Transformers to address field-of-view disparities and achieves favorable performance compared to competitive baselines in experiments on multiple datasets.
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.