## 1 Introduction
There are two advantages to this strategy, including better learning ability and improved generalization capability. In ultrasound imaging, the inherent nature of speckle noise tends to degrade image quality and complicates the distinction between breast tissue and noise artifacts [15], making accurate tumor detection more challenging. Moreover, speckle noise significantly impacts segmentation accuracy by propagating across various convolutional layers at different scales. Current methods primarily leverage the concept of deep supervision to develop refined networks [16], exploring neighboring decisions to correct potential errors induced by speckle noise. However, we propose addressing noise influence from a more fundamental perspective by introducing \"frequency\". In an intriguing experiment, we examined the network's performance variation when eliminating high-frequency information (detail and noise) [17] in deeper layers. We used the mainstream method UNet [18] to evaluate the impact of high frequencies on breast tumor segmentation in the BUSI tests  
Figure 1: Challenges in a breast ultrasound image segmentation task. The red lines are the boundaries of the breast tumors. Inside and outside the boundaries are where the boundaries are blurred. The areas inside the blue circles are typical regions of speckle noise, which refers to irregular, distinct brightness and darkness distribution. (ultrasound images, including both benign and malignant breast tumors) [19]. Building upon [20], we employed multiple pooling operations on the last two stages of the UNet architecture to filter out the high-frequency information and keep only the low-frequency information. As shown in Figure 2, we observed a substantial improvement in model performance when the network solely contained low-frequency information, indicating that speckle noise within high-frequency information disrupts spatial consistency. To address this phenomenon, we introduced a Noise Suppression Module, decoupling high- and low-frequency information in feature maps and denoising the high-frequency components with a Gaussian filter. While following prior works' principles, NSBR-Net also incorporates a deep supervision mechanism. Our method, built upon a transformer-based encoder, incorporates BRM and NSM for breast tumor segmentation in ultrasound images. Its efficacy was validated through extensive experiments on a breast ultrasound dataset and resulted in highlight significant improvement over existing methods. Our contributions include the following:  
* We present a novel breast tumor segmentation framework, termed NSBR-Net. Unlike existing CNN-based methods, we adopt the pyramid vision transformer as an encoder to extract more robust features. * To support our framework, we introduce two simple modules. Specifically, NSM is utilized to suppress speckle noise within high-frequency information, while BRM performs boundary refinement based on coarse regions. * Comparative experiments juxtaposed with leading-edge medical image segmentation models demonstrate the superior efficacy of our method on a breast ultrasound dataset. This paper is structured as follows: Section 2 outlines the dataset, method (including NSM and BRM), loss function, and experimental settings (including evaluation protocols and implementation details). Section 3 presents a qualitative and quantitative comparison of different methods and their corresponding analyses. Section 4 discusses the results, limitations, and future research directions, while Section 5 is dedicated to the conclusion. Figure 2: The network’s performance variation when eliminating high-frequency information. The metrics, including the mean Dice coefficient (mDice) and mean intersection over union (mIoU), both assess the internal consistency of objects within the segmentation results.