# Swin Transformer Assisted Prior Attention Network for Medical Image Segmentation
## 4 Experimental Results
### Ablation Studies  
For further demonstrating the effectiveness of the proposed Swin-PANet, we conduct a series of ablation studies on the network, including three major components in Swin-PANet: (1) Swin Transformer applied in prior attention network (Swin-Trans), (2) attention guiding decoder used for attention fusing (AGD), and (3) enhanced attention block used for aggregation and refining (EAB). The ISIC 2016 dataset is applied to be trained on the proposed method for evaluating the above three modules.  
As shown in Table 2, by integrating Swin-Trans module into baseline, it obtains improvement range from 86.82% (78.23%) to 89.98% (83.43%) in Dice and IoU, respectively. Furthermore, by integrating AGD module into baseline, it obtains improvement range from 86.82% (78.23%) to 89.79% (82.81%) in Dice and IoU, respectively. By the incorporation of AGD and EAB, the Dice metric and IoU metric of methods also increase by a large margin compared with baseline, but they do not exceed the performance of the baseline with Swin-Trans and EAB. This result indicates the great importance of integrating prior attention network with shifted-window attention to improve the performance of skin lesion segmentation. On the other hand, integrating AGD and EAB modules into the baseline obtains improvement range from 89.79% (82.81%) to 90.12% (83.74%) compared with the baseline with AGD, and integrating Swin-Trans, AGD, and EAB modules into the baseline obtains improvement range from 89.98% (83.43%) to 90.68% (84.06%) compared with the baseline with Swin-Trans. These results confirm the effectiveness of enhanced attention block (EAB) and dual supervision strategy in Swin-PANet. Enhanced attention block has the capability of aggregating multiple features from prior attention network (the attention prediction of AGD) and extracting contextual information for further refining boundary.  
Furthermore, the visual comparisons of ablation studies are shown in Figure 6. The first two rows represent the situation with very ambiguous boundaries of skin lesion areas. The third row represents the situation with the existence of hair partially covering the skin lesion and destroying local contextual information. The last row represents the situation with the immense variability of skin lesion including irregular shape and ambiguous boundary. The proposed Swin-PANet can perform better segmentation compared with other combinations with baseline. It can be seen in the second row that the proposed method has the capability of modeling global dependencies between boundary pixels and accurately segmenting the ambiguous boundary.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**ISIC 2016**} \\ \cline{2-3}  & **Dice (\%)** & **IoU (\%)** \\ \hline Baseline (U-Net) & 86.82 & 78.23 \\ Baseline + Swin \(-\) Trans & 89.98 & 83.43 \\ Baseline + AGD & 89.79 & 82.81 \\ Baseline + AGD + EAB & 90.12 & 83.74 \\ Baseline + Swin \(-\) Trans + EAB & 89.56 & 82.50 \\ Baseline + Swin \(-\) Trans +AGD + EAB & 90.68 & 84.06 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experiments on ISIC 2016 dataset. Swin-Trans denotes the Swin Transformer block applied in prior attention network, AGD denotes the attention guiding decoder used for attention fusing, and EAB denotes enhanced attention block used for aggregation and refining.