Supervised Domain Adaptation for Recognizing Retinal Diseases from Wide-Field Fundus Images

• URL: http://arxiv.org/abs/2305.08078v2
• Date: Tue, 24 Oct 2023 02:12:02 GMT
• Title: Supervised Domain Adaptation for Recognizing Retinal Diseases from Wide-Field Fundus Images
• Authors: Qijie Wei, Jingyuan Yang, Bo Wang, Jinrui Wang, Jianchun Zhao, Xinyu Zhao, Sheng Yang, Niranchana Manivannan, Youxin Chen, Dayong Ding, Jing Zhou and Xirong Li
• Abstract summary: This paper addresses the emerging task of recognizing multiple retinal diseases from wide-field (WF) and ultra-wide-field (UWF) fundus images. We propose a supervised domain adaptation method named Cross-domain Collaborative Learning (CdCL) Inspired by the success of fixed-based mixup in unsupervised domain adaptation, we re-purpose this strategy for the current task.
• Score: 23.503104144297684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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