# Cervical Cell Segmentation Method Based on Global Dependency and Local Attention
## 4 Experiment
### Ablation Experiments  
The ablation experiments are divided into two parts, which are to verify the effectiveness of GDLA embedding and to module parallel computing. The GDLA Block is gradually added to U-Net to verify its effectiveness in enhancing the network's ability to extract and utilize feature information. The GDLA is proposed to be embedded in the U-Net encoder section after each stage, and then the refined feature maps are connected to the decoder network by skip connection. Table 3 shows the results \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Nuclei**} & \multicolumn{2}{c}{**Cytoplasm**} & \multirow{2}{*}{**\#Params**} \\ \cline{2-2} \cline{5-6}  & **ZSI** & **Precision** & **Recall** & **ZSI** & **Precision** & **Recall** \\ \hline UNet (baseline) & 0.875 \(\pm\) 0.12 & 0.865 \(\pm\) 0.13 & 0.890 \(\pm\) 0.13 & 0.764 \(\pm\) 0.14 & 0.748 \(\pm\) 0.12 & 0.780 \(\pm\) 0.15 & 13.40 M \\ UNet + GC [38] & 0.894 \(\pm\) 0.11 & 0.877 \(\pm\) 0.13 & 0.912 \(\pm\) 0.12 & 0.781 \(\pm\) 0.13 & 0.767 \(\pm\) 0.13 & 0.786 \(\pm\) 0.12 & 13.74 M \\ UNet + SE [41] & 0.887 \(\pm\) 0.10 & 0.873 \(\pm\) 0.10 & 0.908 \(\pm\) 0.12 & 0.775 \(\pm\) 0.12 & 0.751 \(\pm\) 0.12 & 0.800 \(\pm\) 0.13 & 13.44 M \\ UNet + CBAM [42] & 0.892 \(\pm\) 0.10 & 0.862 \(\pm\) 0.09 & 0.924 \(\pm\) 0.12 & 0.786 \(\pm\) 0.13 & 0.782 \(\pm\) 0.15 & 0.790 \(\pm\) 0.15 & 13.48 M \\ UNet + GDLA (ours) & 0.913 \(\pm\) 0.09 & 0.888 \(\pm\) 0.11 & 0.936 \(\pm\) 0.13 & 0.796 \(\pm\) 0.11 & 0.805 \(\pm\) 0.08 & 0.787 \(\pm\) 0.11 & 14.09 M \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The effect of different modules, including GC unit, SE unit, CBAM module, and GDLA module, on network performance. The values are in the format of \(\mu\pm\sigma\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **ZSI** & **Precision** & **Recall** \\ \hline RGVF algorithm [26] & 0.87 \(\pm\) 0.19 & 0.83 \(\pm\) 0.20 & 0.96 \(\pm\) 0.13 \\ Unsupervised [27] & 0.89 \(\pm\) 0.15 & 0.88 \(\pm\) 0.15 & 0.93 \(\pm\) 0.15 \\ FCM [29] & 0.80 \(\pm\) 0.24 & 0.85 \(\pm\) 0.21 & 0.83 \(\pm\) 0.25 \\ SP-CNN [30] & 0.90 \(\pm\) N/A & 0.89 \(\pm\) N/A & 0.91 \(\pm\) N/A \\ Gap-search MRF [2] & 0.91 \(\pm\) 0.07 & N/A & N/A \\ Mask-RCNN [32] & 0.95 \(\pm\) 0.10 & 0.96 \(\pm\) 0.05 & 0.96 \(\pm\) 0.11 \\ D-MRI [7] & 0.933 \(\pm\) 0.14 & 0.946 \(\pm\) 0.06 & 0.984 \(\pm\) 0.00 \\ PUG-Net [8] & 0.911 \(\pm\) 0.10 & 0.890 \(\pm\) 0.12 & 0.950 \(\pm\) 0.11 \\ HMS 5 [45] & 0.90 \(\pm\) 0.12 & 0.91 \(\pm\) 0.13 & 0.93 \(\pm\) 0.13 \\ Proposed & 0.913 \(\pm\) 0.09 & 0.888 \(\pm\) 0.11 & 0.936 \(\pm\) 0.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of our proposed method with state-of-the-art methods specially designed for cervical cell nuclei segmentation. The values are in the format of \(\mu\pm\sigma\). of integrating the GDLA block at different stages. Furthermore, the results are visualized in Figure 6 for easy observation. The results show that the network performance is steadily improved when the GDLA block is added gradually. When embedding the GDLA module only in s1, the model is comparable to the #params and performance of embed GC block in Table 1, but with both fewer blocks embedded and #params (approximately 0.3 million \(\downarrow\)). Table 4 shows the effects of the three components of the GDLA network on the network performance, and the model feasibility of their combined computation. Nuclei and cytoplasm segmentation were evaluated using several metrics, with ZSI as the main evaluation metric. Here, only the changes in the ZSI evaluation indexes are shown in Table 4, to facilitate the observation of the comparison result. From Table 4, it can be seen that the GD module alone has a greater improvement on the network than the other two modules (approximately 1.9% \(\uparrow\) for nuclei and 1.7% \(\uparrow\) for cytoplasm). The ZSI increases by 1.2% for nuclei and 1.0% for cytoplasm, compared to the baseline when using the CA module alone.