## 5 Experiment and Results Description
### Experiment and Result Analysis
#### 5.4.2 Experiment with the New Network Architecture
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Evaluation Indices** & **PQ** & **PMIoU** & **Precision** & **Recall** & **MIoU** & **F1 Score** &
\begin{tabular}{c} **Dice** \\ **Loss** \\ \end{tabular} \\ \hline LinkNet-No & 16.7 M & 88.2 [48] & 94.95 & 93.43 & 89 & 94.16 & 5.98 \\ LinkNet-DR & & & 91.29 & 87.33 & 81.03 & 89.1 & 11.24 \\ PD-LinkNet-No & & & 94.07 & 93.34 & 88.07 & 93.62 & 6.43 \\ PD-LinkNet-DR & & & 92.65 & 92.88 & 86.43 & 92.67 & 7.54 \\ \hline ResNet-No & 25.5 M & 89.1 [49] & 94.89 & 94.04 & 89.5 & 94.45 & 16.7 \\ ResNet-DR & & & 92.55 & 90.55 & 84.35 & 91.44 & 21.96 \\ PD-ResNet-No & & & 90.97 & 95.14 & 86.88 & 92.91 & 8.34 \\ PD-ResNet-DR & & & 94.82 & 94.83 & 90.05 & 94.74 & 5.51 \\ \hline U-Net-No & 34.4 M & 89.4 [49] & 95.38 & 92.43 & 88.51 & 93.87 & 6.18 \\ U-Net-DR & & & 79.4 & 84.52 & 70 & 81.52 & 18.53 \\ PD-U-Net-No & & & 95.05 & 92.42 & 88.1 & 93.64 & 6.37 \\ PD-U-Net-DR & & & 94.92 & 94.7 & 90.06 & 94.74 & 5.26 \\ \hline DeepLab-No & 64.7M & 91.5 [50] & 93.64 & 94.68 & 89.01 & 94.14 & 5.94 \\ DeepLab-DR & & & 85.99 & 87.84 & 77.34 & 86.73 & 13.43 \\ PD-DeepLab-No & & & 95.16 & 93.15 & 88.86 & 94.07 & 5.94 \\ PD-DeepLab-DR & & & 94.9 & 95.42 & 90.68 & 95.09 & 4.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation results of the network (Unit: %, keep two decimals). \(\xi\)-No and PD-\(\xi\)-DR columns, we find that the networks perform better than the original network. This advantage cannot be achieved by increasing the amount of data, indicating that the network does not have linear invariance, which can be verified by experiments. The convergence of the network can be seen from the Dice loss in the table. By comparing the \(\xi\)-No and \(\xi\)-DR columns, we find that all networks converge when trained on the database without rotation or displacement. When the networks are trained on the data after displacement and rotation, that is, after data enhancement, the losses are 11.24%, 21.96%, 18.53%, and 13.43%, with a minimum loss of 11.24% and a maximum loss of 21.96%. The verification set results show that the network did not converge. However, with the modified PDNet network, regardless of which network is used as the basis, the model converges when trained on the data in the original database or the rotated/displaced data, as shown by the data in the PD-\(\xi\)-DR and PD-\(\xi\)-No columns. The F1 scores also illustrate this problem. The F1 score of PDNet is above 0.9, and the highest value is 0.95. The F1 score of the unmodified network is generally above 0.8, and the lowest value is 0.81. There is also a high F1 score of 0.91; however, the network with this value has a loss of 0.21. A comparison of the above data indicates that the original network does not have rotation invariance. Even after the database is enhanced, the network does not have good segmentation performance on small databases. It is generally believed that the network has linear invariance, which may be an illusion caused by training the network on a large amount of data. Thus, position information should be added to the network to increase its stability and robustness.