# Lightweight Frequency Recalibration Network for Diabetic Retinopathy Multi-Lesion Segmentation
## 1 Introduction  
Diabetic retinopathy (DR), now a prevalent medical issue among diabetics globally, stands as a primary cause of blindness in the working-age population [1]. It is estimated that 93 million individuals worldwide are affected by DR [2, 3]. The condition is characterized by symptoms such as microaneurysms (MAs), hemorrhages (HEs), soft exudates (SEs), and hard exudates (EXs) [4, 5], as illustrated in Figure 1, which are fundamental in ophthalmological diagnoses. At present, there is no absolute cure for this condition. The most efficient approach entails early detection and intervention to control its advancement [6]. In the clinic, ophthalmologists must manually examine lesions in fundus images to screen for DR, which is not only time-intensive but also influenced by the subjective judgment of doctors, posing a challenge to the reliability of detection. Therefore developing an automated method for lesion segmentation is pivotal in the diagnosis of DR. There are considerable variations in the shape, size, and appearance of each lesion type among different individuals, and different lesions may also manifest similar features. MAs and HEs typically present lower intensities in images, while EXs and SEs tend to bebrighter. The intra-class differences and inter-class similarities make the segmentation of DR an exceptionally complex endeavor. The diminished contrast and clarity between a lesion and normal areas challenge segmentation and detection approaches. A variety of image segmentation methods have emerged, and of them, those based on deep learning outperform conventional machine learning techniques. Convolutional neural networks (CNNs) have gained widespread adoption due to their proficiency in extracting intricate layers of features from datasets. Unet and related models [7; 8; 9; 10] are frequently utilized in medical image segmentation. Most of them focus on augmenting the architecture of the original U-Net by embedding attention mechanisms and diverse nonlinear functions within the convolutional layers. Applied in retinal fundus image segmentation, they often are confronted with distractions from inadequate feature extraction. To address this issue, some approaches propose utilizing different domains to extract distinct features. [PERSON] et al. [11] proposed using a high-frequency domain to specify retinal vessel segmentation, which addressed the issue of previous networks being too sensitive to low-frequency noise in fundus images. HGC-Net [12] enhanced vessel segmentation in fundus images by extracting high-frequency components to highlight vascular structures. [PERSON] et al. [13] proposed a network for free-annotated restored cataractous fundus images, which utilized high-frequency components extracted from fundus images to replace segmentation to preserve the retinal structure. However, these methods overlooked the influence of texture information bias in high-frequency domains on segmentation performance. To address these challenges, and inspired by [14], we introduced a model named LFRC-Net to simultaneously segment four types of DR lesions by initially incorporating an improved frequency recalibration module (FRCM) at the top of the encoder to capture a wider range of texture and shape characteristics of lesions. The Laplacian pyramid is integrated into the frequency recalibration module, where the low-frequency domain is taken to learn shape information and the high-frequency area to learn text-based features. The noise is diminished in the final segmentation results. Subsequently, a weighted combination is introduced to aggregate features across all levels of the Laplacian pyramid to obtain the weights through their significance to the segmentation result, which focuses more on features with rich information and suppresses noisy attributes through the global embedding of the channels. Finally, a residual attention module including channel attention and spatial attention is incorporated into the decoder, where the channel attention is used  
Figure 1: Four categories of DR lesions.