## 3 Results  
### Comparison with State-of-the-Art Methods  
The quantitative evaluation results on the BUSI dataset are presented in Table 1. For breast tumors with indistinct boundaries, NSBR-Net achieves consistent improvements against baseline models under comparable mDice, mIoU, and MAE values. In clinical diagnosis, the segmentation and localization of malignant tumors are paramount. For malignant tumors, these metrics are improved by 2.3%, 2.39%, and 0.23% compared to the second-best results. Compared to AAU-Net [4] in terms of mDice, our model achieves a 5.32% improvement in malignant tumors.  
We demonstrate the qualitative performance of our model on Dataset B in Table 2. Our model achieves good performance across the entire dataset, with an additional improvement of 3.67% in mDice specifically in the testing of malignant tumors compared to the transformer-based method, DuAT [11].  
The visualization of our method and comparative methods on the BUSI dataset and Dataset B is shown in Figure 6. Our method has the best performance in segmenting tumors. For instance, in the case of blurred tumor boundaries (first row and third row), other methods exhibit significant instances of false negatives, while our method addresses this issue well. Specifically, the reason lies in the BRM's ability to extract complementary regions and details of tumors and enhance the saliency analysis of intermediate features through the attention mechanism, enabling increased focus on object regions. Similarly, under speckle noise (second row and fourth row) conditions, NSBR-Net exhibits no issues with false positives.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**All**} & \multicolumn{3}{c}{**Benign**} & \multicolumn{3}{c}{**Malignant**} \\ \cline{2-10}
**Method** & **mDice \(\uparrow\)** & **mIoU \(\uparrow\)** & \(MAE\)\(\downarrow\)** & **mDice \(\uparrow\)** & **mIoU \(\uparrow\)** & \(MAE\)\(\downarrow\)** & **mDice \(\uparrow\)** & **mIoU \(\uparrow\)** & \(MAE\)\(\downarrow\)** \\ \hline UNet [18] & 0.6943 & 0.6033 & 0.0496 & 0.7219 & 0.6362 & 0.0380 & 0.6232 & 0.5183 & 0.0798 \\ Attention U-Net [32] & 0.6934 & 0.6016 & 0.0509 & 0.7247 & 0.6374 & 0.0378 & 0.6125 & 0.5092 & 0.0845 \\ UNet++ [1] & 0.7023 & 0.6070 & 0.0509 & 0.7212 & 0.6301 & 0.0398 & 0.6538 & 0.5476 & 0.0796 \\ UNet3+ [2] & 0.7055 & 0.6139 & 0.0493 & 0.7358 & 0.6433 & 0.0388 & 0.6487 & 0.5414 & 0.0765 \\ PraNet [23] & 0.7698 & 0.6847 & 0.0413 & 0.7841 & 0.7037 & 0.0320 & 0.7330 & 0.6272 & 0.0654 \\ DoubleU-Net [33] & 0.7735 & 0.6870 & 0.0461 & 0.8016 & 0.7179 & 0.0333 & 0.7010 & 0.5885 & 0.0790 \\ UACANet [25] & 0.7473 & 0.6650 & 0.0442 & 0.7593 & 0.6773 & 0.0353 & 0.7163 & 0.6089 & 0.0672 \\ SANet [34] & 0.7708 & 0.6842 & 0.0458 & 0.7929 & 0.7074 & 0.0351 & 0.7136 & 0.6065 & 0.0732 \\ UNext [35] & 0.7171 & 0.6258 & 0.0436 & 0.7366 & 0.6509 & 0.0332 & 0.6668 & 0.5613 & 0.0702 \\ CaraNet [36] & 0.7769 & 0.6968 & 0.0383 & 0.7947 & 0.7199 & 0.0287 & 0.7289 & 0.6267 & 0.0633 \\ AAU-Net [4] & 0.7751 & 0.6882 & – & 0.8088 & 0.7333 & – & 0.7154 & 0.6060 & – \\ DuAT [11] & 0.8017 & 0.7163 & 0.0406 & 0.7864 & 0.7037 & 0.0314 & 0.7185 & 0.6104 & 0.0767 \\ PVT-CASCADE [10] & 0.8118 & 0.7270 & 0.0380 & 0.8374 & 0.7582 & 0.0245 & 0.7456 & 0.6465 & 0.0619 \\ NSBR-Net (Ours) & 0.8183 & 0.7350 & 0.0355 & 0.8375 & 0.7601 & 0.0262 & 0.7686 & 0.6704 & 0.0596 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative comparison of different methods on BUSI [19] to validate our model’s learning ability. \(\uparrow\) denotes the higher the better, and \(\downarrow\) denotes the lower the better. Red indicates the best results and blue represents the second-best results.