## 4 Experiments
### Ablation Study
#### 4.5.2 Selection of Convolution Parameters in the Demodulate Bridge  
The data dimensions of feature maps at different stages vary. It is essential to select appropriate convolution kernels for feature maps of different sizes to adequately filter out redundant information within the feature maps. As shown in Table 5, it is necessary to use the Demodulate Bridge to filter out redundant information in the feature maps. Plan 4, which did not use any convolution operations to filter information in the feature maps, achieved the worst results. Plan 2 used overly large convolution kernels to filter redundant information. Plan 3 used convolution kernels of the same size for feature maps at all stages, resulting in the second-best performance. In summary, using convolution kernels of sizes 7, 5, 3, and 1 to filter feature maps at the four stages is a balanced choice.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**MaxConvKernel**} & \multirow{2}{*}{**Dice \(\uparrow\)**} & \multirow{2}{*}{**Aorta \(\uparrow\)**} & **Gallbladder** & **Kidney** & **Kidney** & **Liver \(\uparrow\)** & **Pancreas** & **Spleen \(\uparrow\)** & **Stomach** \\  & & & \(\uparrow\) & **(L) \(\uparrow\)** & **(R) \(\uparrow\)** & **(R) \(\uparrow\)** & \(\uparrow\)** & \(\uparrow\) & **Split** \(\uparrow\) & \(\uparrow\) \\ \hline \(H\) & 80.02 & 84.49 & 55.51 & 72.91 & 87.68 & **96.53** & 67.45 & 90.26 & 85.32 \\ \(\frac{3H}{4}\) & 80.50 & 86.51 & 58.03 & 73.21 & **87.71** & 96.01 & 67.45 & 90.13 & **84.91** \\ \(\frac{H}{2}\) & **84.74** & 87.62 & 73.95 & **89.27** & 86.47 & 95.38 & 69.72 & **90.66** & 84.85 \\ \(\frac{H}{4}\) & 81.30 & **87.93** & **74.01** & 70.03 & 85.31 & 89.76 & **69.92** & 90.18 & 83.29 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Impact of the Maximum Convolution Kernel in the Convolution Group on MINTFormer’s experimental results on the Synapse dataset, best result is highlight in red, \(\uparrow\) indicates higher values are better.  
\begin{table}
\begin{tabular}{l l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Backbone**} & \multicolumn{4}{c}{**ISIC2017**} & \multicolumn{4}{c}{**ISIC2018**} & \multicolumn{4}{c}{**PH2**} \\ \cline{3-14}  & & & **Dice \(\uparrow\)** & **SE \(\uparrow\)** & **SP \(\uparrow\)** & **ACC \(\uparrow\)** & **Dice \(\uparrow\)** & **SE \(\uparrow\)** & **SP \(\uparrow\)** & **ACC \(\uparrow\)** & **Dice \(\uparrow\)** & **SE \(\uparrow\)** & **SP \(\uparrow\)** & **ACC \(\uparrow\)** \\ \hline U-Net[4] & CNN & 81.59 & 81.72 & 96.80 & 91.64 & 85.45 & 88.00 & 96.97 & 94.04 & 89.36 & 91.25 & 95.88 & 92.33 \\ Att-Unet[61] & CNN & 80.82 & 79.98 & 97.76 & 91.45 & 85.66 & 86.74 & 96.83 & 93.76 & 90.03 & 92.05 & 96.40 & 92.76 \\ TransUNet[7] & CNN+Transformer & 81.23 & 82.63 & 95.77 & 92.07 & 84.99 & 85.78 & 96.53 & 94.52 & 88.84 & 90.63 & 94.27 & 92.00 \\ HiFormer[72] & CNN+Transformer & 92.53 & 91.55 & 98.40 & 97.02 & 91.92 & 91.97 & 97.55 & 96.21 & 94.60 & 94.20 & 97.72 & 96.61 \\ Swin-UNet[35] & Transformer & 91.83 & 91.42 & 97.98 & 97.01 & 89.46 & 90.56 & **97.98** & **96.45** & 94.49 & 94.10 & 95.64 & 96.78 \\ MissFormer[31] & Transformer & 89.03 & 89.24 & 97.25 & 95.69 & 91.01 & 90.31 & 97.45 & 94.42 & 94.01 & 90.35 & 96.91 & 96.14 \\ TMU[73] & Transformer & 91.64 & 91.28 & 97.89 & 96.60 & 90.59 & 90.38 & 97.46 & 96.03 & 91.14 & 93.95 & 97.56 & 96.47 \\ CSWi-Unet[20] & Transformer & 91.47 & 93.79 & **98.56** & **97.26** & 91.11 & **92.31** & 97.88 & 95.25 & 94.29 & **95.63** & **97.82** & **96.82** \\ \hline MINTFormer & Heterogeneous Transformer & **92.71** & **94.38** & 97.73 & **97.07** & **91.59** & 90.63 & **98.81** & **96.42** & **94.81** & 95.59 & **98.01** & **96.89** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of MINTFormer on skin lesion segmentation datasets, best result is highlight in red, and the second best result is highlight in blue, \(\uparrow\) indicates higher values are better.