WSRFNet: Wavelet-Based Scale-Specific Recurrent Feedback Network for Diabetic Retinopathy Lesion Segmentation

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.