## 3 Results
### Ablation Study  
**Effectiveness of Different Network Components. ** In Table 4, we employ a transformer-based encoder combined with a partial decoder (PD) as our baseline. Note that our partial decoder is only deployed on the high-level features, which achieve an mDice score of 78.79%. This not only showcases the effectiveness of the transformer encoder but also highlights a natural advantage over conventional CNN-based methods. We further investigate the contribution of the Noise Suppression Module. We observe that adding NSM improves the baseline performance, increasing the mDice score from 78.79% to 80.31%. These improvements suggest that introducing an NSM component can enable our model to enhance the quality of global maps. We verify the performance enhancement after integrating the Boundary Refinement Module. A noticeable improvement of 1.93% in the mDice score compared to the baseline is observed. This substantiates that BRM enables our model to accurately distinguish breast tumors. Finally, by simultaneously integrating the two primary components, we achieve a performance boost of 3.04%. This indicates that the fusion of high-quality coarse-grained information with refined boundary recovery is crucial and indispensable for localizing breast tumors. To further validate the boundary refinement capability of BRM, we present the visualization of ablative experiment results in Figure 7. We selected tumors of varying sizes for analysis, with the first and third rows representing small tumors and the second and fourth rows representing large tumors. It can be observed that regardless of the size of the tumor, the segmentation results of the model incorporating BRM (second and fourth columns) are better fitted compared to those without BRM (third column). This further confirms that BRM indeed refines the boundary segmentation results, enabling more accurate localization of lesion boundaries, which is crucial for clinical applications. **Quantitative Comparison of Variants of NSM. ** As reported in Table 5, when show-casing variations in the NSM module, we aimed to demonstrate the impact of frequency information on speckle noise suppression. It is noteworthy that retaining only the low-pass filter within the NSM resulted in exceptional performance, surpassing an mDice score of 81%. However, introducing high-frequency information led to performance degradation, indicating that the cumulative noise error within the high-frequency data impaired the model's performance. Additionally, leveraging Gaussian filtering on top of the high-frequency filter effectively mitigated noise errors, preserving valuable high-frequency information and contributing to a 0.76% performance boost. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**PD** & **NSM** & **BRM** & **mDice (\%)** & **mIoU (\%)** \\ \hline ✓ & & & 78.79 & 70.16 \\ ✓ & ✓ & & 80.31 (+1.52) & 71.49 (+1.33) \\ ✓ & & ✓ & 80.72 (+1.93) & 72.10 (+1.94) \\ ✓ & ✓ & ✓ & 81.83 (+3.04) & 73.50 (+3.34) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study on the effectiveness of different components on the BUSI dataset. Red values enclosed in parentheses refer to improvement compared with partial decoder. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **mDice \(\uparrow\)** & **mIoU \(\uparrow\)** & \(MAE\downarrow\) \\ \hline UNet [18] & 0.7838 & 0.6834 & 0.0492 \\ Attention U-Net [32] & 0.7476 & 0.6380 & 0.0561 \\ CaraNet [36] & 0.7708 & 0.6635 & 0.0434 \\ DuAT [11] & 0.8739 & 0.7842 & 0.0275 \\ NSBR-Net (Ours) & 0.8877 & 0.8048 & 0.0236 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of models trained on Dataset B [21] and evaluated on STU [37]. \(\uparrow\) denotes the higher the better and \(\downarrow\) denotes the lower the better. Red indicates the best results and blue represents the second-best results. **Quantitative Comparison of Backbone Architecture. ** As reported in Table 6, we present the performance of our proposed NSBR-Net method with different backbone architectures on the BUSI dataset.