{"type":"ScholarlyArticle","title":"WSRFNet: Wavelet-Based Scale-Specific Recurrent Feedback Network for Diabetic Retinopathy Lesion Segmentation","authors":[{"name":"Xuan Li","slug":"xuan-li","given":"Xuan","family":"Li","affiliation":"Faculty of Computing , Harbin Institute of Technology , Harbin , China"},{"name":"Xiangqian Wu","slug":"xiangqian-wu","given":"Xiangqian","family":"Wu","affiliation":"Faculty of Computing , Harbin Institute of Technology , Harbin , China"}],"abstract":"Diabetic retinopathy lesion segmentation (DRLS) faces a challenge of significant variation in the size of different lesions. An effective method to address this challenge is to fuse multi-scale features. To boost the performance of this kind of method, most existing DRLS methods work on devising sophisticated multi-scale feature fusion modules. Differently, we focus on improving the quality of the multi-scale features to enhance the fused multi-scale feature representation. To this end, we design a Wavelet-based Scale-specific Recurrent Feedback Network (WSRFNet), which refines multi-scale features using recurrent feedback mechanism. Specifically, to avoid information loss when introducing feedback to multi-scale features, we propose a wavelet-based feedback pyramid module (WFPM), which is based on a reversible downsampling operation, i.e., Haar wavelet transform. Unlike scale-agnostic feedback used in previous feedback methods, we develop a scale-specific refinement module (SRM), which utilizes scale-specific feedback to pointedly refine features of different scales. Experimental results on IDRiD and DDR datasets show that our approach outperforms state-of-the-art models. The code is available at https://github.com/xuanli01/WSRFNet.","doi":"10.24963/ijcai.2024/115","url":"https://ijcai.org/proceedings/2024/115","pdf":"https://ijcai.org/proceedings/2024/0115.pdf","markdown":"https://ijcai.org/proceedings/2024/115.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/115","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":115,"pages":{"first":1038,"last":1046},"keywords":["Computer Vision: CV: Segmentation","Computer Vision: CV: Biomedical image analysis"],"videos":[],"references":[{"position":1,"raw":"[Bo et al., 2022] Wang Bo, Tao Li, Xinhui Liu, and Kai Wang. 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