# Transformer-Based Explainable Model for Breast Cancer Lesion Segmentation
## 4 Results
### Quantitative Comparison with State-of-The-Art Models  
We compared the segmentation performance of the proposed TEBLS model with previous segmentation models on the CBIS-DDSM dataset, including Density-ASP [39], U-Net [14], Res-Basic-U-Net [15], and AU-Net [40]. The results in terms of the mean DSC are presented in Table 4. With the same number of epochs, the segmentation performance of the TEBLS model improved by 35.3%, 21.8%, and 28.5%, respectively, compared to  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Mean DSC** & **Mean AUC** & **Epoch** \\ \hline Unet++ & 89.52 & 83.00 & 150 \\ Swin-Unet++ & 99.30 & 68.09 & 150 \\ TEBLS & 81.86 & 97.72 & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study results comparing model performance on the test dataset in terms of the mean DSC and mean AUC.  
that of the U-Net, AU-Net, and Res-Basic-U-Net models, which are based on simple skip connections, adaptive attention mechanisms, and residual connections.  
Comparing the segmentation results of the U-Net model to those of Res-Basic-U-Net and AU-Net showed that AU-Net achieved the best performance. This was due to AU-Net's incorporation of a self-attention mechanism, which enhanced the learning of small targets and complex background features, in contrast to the residual network used in Res-Basic-U-Net. Additionally, when comparing AU-Net with Density-ASP, which uses AU-Net as its baseline, AU-Net outperformed Density-ASP. This was because Density-ASP dynamically adjusted the training priority based on sample density, making its segmentation outcomes highly dependent on dataset characteristics. In contrast, the proposed TEBLS model integrates a transformer-based structure with the Unet++ model and further improved feature learning through multi-channel feature fusion, resulting in superior segmentation performance.  
Table 5 presents the accuracy of the proposed TEBLS model compared to that of Swin-Unet, Vision Transformer (ViT) [17], and the Inceptionv3 model on the CBIS-DDSM dataset. The mean AUC represents the average AUC value obtained over multiple iterations, serving as a measure of the model's performance. As shown in Table 5, the proposed TEBLS model demonstrated a significant improvement in the mean AUC compared to the transformer-based Swin-Unet and ViT models. This improvement is attributed to the dense nested feature fusion in the TEBLS model, which enabled it to capture features at different levels and enhance feature representation through fusion.  
Moreover, the TEBLS model outperformed the Inceptionv3 model in terms of the mean AUC. This was due to the inherent limitations of Inceptionv3, a CNN-based classification model, which relies on convolutional kernels and local receptive fields. These constraints reduce its effectiveness in capturing global features, particularly for tasks requiring long-range dependency modeling and a comprehensive understanding of global context. In contrast, transformer-based architectures leverage a global self-attention mechanism, enabling the more effective extraction of global features and long-range dependencies.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Mean DSC** & **Epoch** \\ \hline UNet & 73.60 & 150 \\ Res-Basic-UNet & 77.50 & 150 \\ AU-Net & 81.80 & 150 \\ Density-ASP & 50.64 & 150 \\ TEBLS & 81.86 & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 4: A quantitative comparison of segmentation results on the test dataset was conducted, including U-Net, Res-Basic-U-Net, AU-Net, Density-ASP, and our proposed method, TEBLS. All methods were trained on the same dataset under identical conditions and evaluated on the same test data.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Mean AUC** & **Epoch** \\ \hline Swin-Unet & 60.57 & 150 \\ ViT & 61.74 & 150 \\ Inceptionv3 & 66.02 & 150 \\ TEBLS & 97.72 & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative comparison of classification results, including Swin-Unet, ViT, Inceptionv3, and our proposed method, TEBLS.