## 4 Experiments
### Efficiency Analysis  
To comprehensively evaluate the advantages and disadvantages of MINTFormer, we conducted a performance analysis using the ISIC2017 dataset on the Nvidia A100 platform (Nvidia, Beijing, China). As shown in Table 6, the Large model of MINTFormer achieved the best results but also occupied the most parameters, with 122.12 M parameters that require an Nvidia 4080 (Colorful, Beijing, China) or higher level compute card to operate. The Middle model, which can be run on an Nvidia 4070 or equivalent mid-level compute card, achieved sub-optimal results. The Small model requires an Nvidia 4060 or equivalent compute card and has the lowest performance. In our clinical setting, the inference hardware used is the Nvidia 4080 compute card, which can fully support the inference tasks of MINTFormer's Large model.  
Furthermore, we plotted the energy efficiency curves for MINTFormer, using the ISIC2017 dataset for comparison with advanced models such as MissFormer [31], Swin-Unet [35], Unet++ [29], and ST-Unet [68]. As shown in Figure 12, MINTFormer's energy efficiency curves for the middle model scale (parameter range of 70-90 M) and large model scale (parameters over 90 M) are superior to those of other advanced models. However, in the case of a smaller model scale (parameters less than 70 M), it is inferior to other models. This indicates that MINTFormer possesses strong information-capturing capabilities but lacks in lightweighting work.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model Scale**} & \multirow{2}{*}{**Heterogeneous Encoder Plan**} & \multicolumn{4}{c}{**ISIC2017**} & \multirow{2}{*}{**ACC \(\uparrow\)**} & \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Flops**} & \multirow{2}{*}{**FPS**} \\  & & **Dice \(\uparrow\)** & **SE \(\uparrow\)** & **SP \(\uparrow\)** & & & \\ \hline \multirow{2}{*}{Large} & CSWin [22]+MaxViT [23] & 92.71 & 94.38 & 97.73 & 97.07 & 122.12 M & 814.16 G & 5131.12 \\ \hline \multirow{4}{*}{Middle} & CSWin [22]+EfficientFormer [74] & 88.96 & 89.61 & 86.50 & 83.75 & 79.17 M & 655.47 G & 6230.04 \\  & CSWin [22]+MobileViTv3 [75] & 87.15 & 88.13 & 84.99 & 82.09 & 69.25 M & 607.35 G & 6833.45 \\ \cline{1-1}  & MaxViT [23]+EfficientFormer [74] & 90.70 & 92.11 & 88.03 & 87.10 & 85.77 M & 718.38 G & 5595.21 \\ \cline{1-1}  & MaxViT [23]+MobileViTv3 [75] & 89.69 & 86.29 & 83.46 & 85.94 & 78.34 M & 702.74 G & 5873.37 \\ \hline Small & MobileViTv3 [75]+EfficientFormer [74] & 65.38 & 64.59 & 74.86 & 73.58 & 65.49 M & 533.21 G & 8907.76 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Result of efficiency analysis, best result is highlight in red, \(\uparrow\) indicates higher values are better, M stands for million and G stands for billion.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Stage1** & **Stage2** & **Stage3** & **Stage4** & **Dice \(\uparrow\)** & **HD95 \(\downarrow\)** \\ \hline plan1 & 7 & 5 & 3 & 1 & 84.7 & 14 \\ plan2 & 9 & 7 & 5 & 3 & 80.1 & 21.5 \\ plan3 & 3 & 3 & 3 & 3 & 83.7 & 13.5 \\ plan4 & None & None & None & None & 75.3 & 25.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Impact of convolution parameters in the Demodulate Bridge on MINTFormer’s experimental results on the Synapse dataset, \(\uparrow\) indicates higher values are better. \(\downarrow\) indicates lower values are better.