# PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism
## 4 Results
### Ablation Analysis  
Use of PAM-UNet demonstrates that PAM, DropBlock_Diagonal, and MFMM can enhance the segmentation performance of the algorithm to some extent based on ablation experiments conducted on the DRIVE dataset. To ensure the fairness of the experiments, all comparison methods employ the same training strategy and hyperparameter settings. Table 6 presents the results of the ablation experiments. As shown in Table 6, when PAM is added to the BottleNeck of the network, F1 increases by 0.06%. This indicates that the addition of PAM allows the network to better learn and focus on vascular structures of different shapes and sizes. When DropBlock_Diagonal is added to the network, F1 increases by 0.14% and AUC by 0.03%. This indicates that the inclusion of DropBlock_Diagonal enables the network to better learn vascular features. At this point, the parameters and FLOPs of the network remain unchanged, as DropBlock_Diagonal only functions during model training. When MFMM is added to the model, F1 increases by 0.01% and AUC by 0.03%. This indicates that merging feature maps at each stage of the decoder improves the model's quality and segmentation capability. When both PAM and DropBlock_Diagonal are added to the model, F1 increases by 0.18% and AUC by 0.04%. This demonstrates that the combination of PAM and DropBlock_Diagonal is superior to adding PAM or DropBlock_Diagonal individually, further proving the effectiveness of PAM and DropBlock_Diagonal. When both DropBlock_Diagonal and MFMM are added to the model, F1 increases by 0.15% and AUC by 0.06%. This shows that the combination of DropBlock_Diagonal and MFMM is superior to adding DropBlock_Diagonal or \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **F1/\%** & **AUC/\%** & **Parameters** & **FLOPs** \\ \hline Baseline & 82.87 & 98.56 & **4.28 M** & **153.39 G** \\ \hline Baseline + PAM & 82.93 & 98.56 & 4.29 M & 153.39 G \\ \hline Baseline + Diag & 83.01 & 98.59 & 4.28 M & 153.39 G \\ \hline Baseline + MFMM & 82.88 & 98.59 & 4.29 M & 154.36 G \\ \hline Baseline + PAM + Diag & 83.05 & 98.60 & 4.29 M & 153.39 G \\ \hline Baseline + Diag + MFMM & 83.02 & 98.62 & 4.29 M & 154.36 G \\ \hline Baseline + PAM + MFMM & 82.94 & 98.60 & 4.30 M & 154.36 G \\ \hline PAM-UNet & **83.15** & **98.66** & 4.30 M & 154.36 G \\ \hline \hline \end{tabular} The bold font indicates the optimal value.  
\end{table} 
Table 6: Results of ablation experiments. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **Acc/\%** & **Se/\%** & **Sp/\%** & **F1/\%** & **AUC/\%** \\ \hline UNet [13] & 95.78 & 82.88 & 97.01 & 77.83 & 97.72 \\ R2U-Net [16] & 96.34 & 77.56 & 98.20 & 79.28 & 98.15 \\ LadderNet [48] & 96.56 & 79.78 & 98.18 & 80.31 & 98.39 \\ [PERSON]’s model [49] & 96.35 & 78.18 & 98.19 & - & 98.10 \\ SCS-Net [21] & 97.44 & 83.65 & 98.39 & - & 98.67 \\ ResDo-UNet [23] & 96.72 & 80.20 & 97.94 & 82.36 & - \\ NAUNet [50] & 96.83 & 80.71 & 98.20 & - & 98.41 \\ SegR-Net [51] & - & 83.29 & 98.38 & 80.30 & - \\ LUVS-Net [52] & 97.38 & 82.69 & 98.46 & - & 81.27 \\ OCE-Net [25] & 96.78 & 81.38 & 98.24 & 81.96 & 98.72 \\ MU-Net [27] & 97.52 & 83.13 & **98.49** & - & 98.60 \\ PAM-UNet & **97.65** & **85.82** & 98.46 & **82.56** & **98.95** \\ \hline \hline \end{tabular} The bold font indicates the optimal value.  
\end{table} 
Table 5: Comparison of PAM-UNet with other algorithms on CHASE_DB1 dataset. MFMM individually, further proving the effectiveness of DropBlock_Diagonal and MFMM. When both PAM and MFMM are added to the model, F1 increases by 0.07% and AUC by 0.04%. This shows that the combination of PAM and MFMM is superior to adding PAM or MFMM individually, further proving the effectiveness of PAM and MFMM. When PAM, DropBlock_Diagonal, and MFMM are added to the model, F1 and AUC reach their optimal values of 83.15% and 98.66%, respectively. Ablation experiments on the DRIVE dataset indicate that the designed modules each improve the network's segmentation performance to varying degrees.