# AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation
## 3 Results
### Comparison Experiment with SOTA Deep Learning Methods
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Dataset** & **Method** & **Dice** & **IoU** & **Accuracy** & **Specificity** & **Sensitivity** & **Precision** \\ \hline \multirow{8}{*}{HAM10000} & UNet & 0.9235 & 0.8604 & 0.9256 & 0.9350 & 0.9260 & 0.9237 \\  & \({}^{\star}\)UNet & 0.9356 & 0.8811 & 0.9381 & 0.9532 & 0.9384 & 0.9360 \\  & UNet++ & 0.9191 & 0.8533 & 0.9220 & 0.9411 & 0.9226 & 0.9209 \\  & \({}^{\star}\)UNet++ & 0.9396 & 0.8883 & 0.9413 & 0.9413 & 0.9402 & 0.9407 \\  & \({}^{\star}\)UNet & 0.9217 & 0.8576 & 0.9238 & 0.9217 & 0.9249 & 0.9213 \\  & \({}^{\star}\)nn-UNet & 0.9295 & 0.8705 & 0.9317 & 0.9574 & 0.9310 & 0.9317 \\  & Trans UNet & 0.9006 & 0.8235 & 0.9040 & 0.9207 & 0.9039 & 0.9035 \\  & \({}^{\star}\)Trans UNet & 0.9267 & 0.8674 & 0.9303 & 0.9223 & 0.9295 & 0.9275 \\  & \({}^{\star}\)Attention-UNet & 0.9083 & 0.8353 & 0.9113 & 0.9039 & 0.9123 & 0.9093 \\  & \({}^{\star}\)Attention-UNet & 0.9219 & 0.8574 & 0.9249 & 0.9129 & 0.9243 & 0.9224 \\ \hline \multirow{8}{*}{Blood Cell} & UNet & 0.9759 & 0.9541 & 0.9790 & 0.9831 & 0.9767 & 0.9757 \\  & \({}^{\star}\)UNet & 0.9806 & 0.9623 & 0.9841 & 0.9874 & 0.9811 & 0.9804 \\  & UNet++ & 0.9700 & 0.9442 & 0.9739 & 0.9827 & 0.9701 & 0.9727 \\  & \({}^{\star}\)UNet++ & 0.9790 & 0.9593 & 0.9826 & 0.9870 & 0.9794 & 0.9789 \\  & nn-UNet & 0.9775 & 0.9573 & 0.9817 & 0.9834 & 0.9798 & 0.9775 \\  & \({}^{\star}\)nn-UNet & 0.9785 & 0.9582 & 0.9826 & 0.9836 & 0.9803 & 0.9776 \\  & Trans UNet & 0.9668 & 0.9373 & 0.9732 & 0.9697 & 0.9690 & 0.9667 \\  & \({}^{\star}\)Trans UNet & 0.9781 & 0.9574 & 0.9820 & 0.9844 & 0.9799 & 0.9766 \\  & Attention-UNet & 0.9720 & 0.9462 & 0.9762 & 0.9779 & 0.9746 & 0.9700 \\  & \({}^{\star}\)Attention-UNet & 0.9779 & 0.9573 & 0.9816 & 0.9878 & 0.9770 & 0.9791 \\ \hline \multirow{8}{*}{COVID-19} & UNet & 0.9106 & 0.8490 & 0.9900 & 0.9948 & 0.9173 & 0.9066 \\  & \({}^{\star}\)UNet & 0.9177 & 0.8593 & 0.9926 & 0.9959 & 0.9239 & 0.9134 \\ \cline{1-1}  & UNet++ & 0.9042 & 0.8403 & 0.9898 & 0.9942 & 0.9099 & 0.9057 \\ \cline{1-1}  & \({}^{\star}\)UNet++ & 0.9059 & 0.8426 & 0.9908 & 0.9958 & 0.9002 & 0.9108 \\ \cline{1-1}  & \({}^{\star}\)n-UNet & 0.9038 & 0.8384 & 0.9901 & 0.9954 & 0.8991 & 0.9146 \\ \cline{1-1}  & \({}^{\star}\)nn-UNet & 0.9149 & 0.8543 & 0.9908 & 0.9948 & 0.9213 & 0.9106 \\ \cline{1-1}  & Trans UNet & 0.8944 & 0.8272 & 0.9911 & 0.9962 & 0.8881 & 0.9045 \\ \cline{1-1}  & \({}^{\star}\)Trans UNet & 0.8960 & 0.8275 & 0.9897 & 0.9941 & 0.9056 & 0.8907 \\ \cline{1-1}  & Attention-UNet & 0.8978 & 0.8306 & 0.9895 & 0.9943 & 0.9026 & 0.9853 \\ \cline{1-1}  & \({}^{\star}\)Attention-UNet & 0.9110 & 0.8487 & 0.9911 & 0.9964 & 0.8990 & 0.9267 \\ \hline \multirow{8}{*}{Kvasir} & UNet & 0.9015 & 0.8309 & 0.9488 & 0.9746 & 0.8984 & 0.9074 \\ \cline{1-1}  & \({}^{\star}\)UNet & 0.9172 & 0.8531 & 0.9360 & 0.9515 & 0.9206 & 0.9167 \\ \cline{1-1}  & UnNet++ & 0.8909 & 0.8146 & 0.9368 & 0.9425 & 0.9263 & 0.8685 \\ \cline{1-1}  & \({}^{\star}\)UNet++ & 0.8986 & 0.8276 & 0.9489 & 0.9807 & 0.8864 & 0.9175 \\ \cline{1-1}  & nn-UNet & 0.8925 & 0.8172 & 0.9440 & 0.9720 & 0.8887 & 0.8999 \\ \cline{1-1}  & \({}^{\star}\)nn-UNet & 0.9026 & 0.8315 & 0.9444 & 0.9503 & 0.9318 & 0.8809 \\ \cline{1-1}  & Trans UNet & 0.8554 & 0.7655 & 0.9234 & 0.9442 & 0.8813 & 0.8411 \\ \cline{1-1}  & \({}^{\star}\)Trans UNet & 0.8849 & 0.8060 & 0.9362 & 0.9552 & 0.8961 & 0.8802 \\ \cline{1-1}  & Attention-UNet & 0.8547 & 0.7641 & 0.9155 & 0.9300 & 0.8902 & 0.8317 \\ \cline{1-1}  & \({}^{\star}\)Attention-UNet & 0.8953 & 0.8222 & 0.9473 & 0.9777 & 0.8840 & 0.9110 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of segmentation performance across four datasets using five models (UNet, UNet++, nn-UNet, TransUNet, and Attention-UNet) with standard convolution and AdaptiveConv2d. high-quality datasets. The small but consistent improvements indicate that AdaptiveConv2d has the potential to further enhance model performance even when baseline results are already high.