## 4 Experiments and Results
### Experimental Results and Analysis
#### 4.5.5 Ablation Experimental  
In this experiment, the standard VGG16 is used as a baseline, and the effectiveness of different modules is validated by inserting them. MLNet's AUPR scores on the DDR dataset with different network structure settings are shown in Table 4.  
According to Table 4, a comparative analysis reveals the performance improvements achieved by different variations of the model. Compared to No.1, No.2 improves the mAUPR from 32.43% to 37.48% and improves the segmentation results of MAs, SEs, HEs, \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**AUPR/\%**} \\ \cline{2-6}  & **MA** & **SE** & **HE** & **EX** & **mAUPR** \\ \hline (No.1) Baseline + Proposed loss & 24.49 & 23.81 & 36.23 & 45.20 & 32.43 \\ (No.2) Baseline + MSAB + Proposed loss & 28.01 & 33.24 & 37.49 & 51.17 & 37.48 \\ (No.3) Baseline + LPPs + Proposed loss & 29.34 & 35.49 & 38.11 & 53.92 & 39.22 \\ (No.4) Baseline + MSAB + LPB + Dice loss & 30.18 & 45.41 & 52.82 & 63.67 & 48.02 \\ (No.5) MLNet (Baseline + MSAB + LPB + Proposed loss) & **34.28** & **48.41** & **57.39** & **67.16** & **51.81** \\ \hline \hline \end{tabular} Bold values indicate the best-performing results.  
\end{table} 
Table 4: The ablation experimental results of MLNet on the DDR dataset. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**AUPR/\%**} \\ \cline{2-6}  & **MA** & **SE** & **HE** & **EX** & **mAUPR** \\ \hline M2 MRF [22] & 44.09 & 55.20 & 59.97 & **74.04** & 58.33 \\ L-Seg [37] & 19.86 & 30.59 & 44.05 & 65.01 & 39.88 \\ RTNet [9] & 21.14 & 34.01 & 45.04 & 67.99 & 42.05 \\ HEDNet [38] & 17.16 & 31.78 & 39.68 & 57.66 & 36.57 \\ MLNet & **49.56** & **61.08** & **60.91** & 66.60 & **59.54** \\ \hline \hline \end{tabular} Bold values indicate the best-performing results.  
\end{table} 
Table 3: Generalization results of different methods on IDRiD dataset. and EXs by 3.52%, 9.43%, 1.26%, and 5.97%, respectively. These results highlight the positive impact of incorporating the additional components on the overall segmentation performance. Additionally, despite both incorporating the MSAB, compared to No.3, No.4 shows an improvement range of 4.94% to 19.82% in the AUPR, with an 11.93% increase in the mAUPR. When the LPB is added to the baseline (No.3), the mAUPR significantly increases from 32.43% to 39.22%. For the segmentation results of MAs, SEs, HEs, and EXs, the AUPR improves by 4.85%, 11.68%, 1.88%, and 8.72%, respectively. No.4 also exhibits a similar performance improvement trend compared to No.2, further demonstrating the effective combination of the MSAB and the LPB in generating the optimal segmentation results. The inclusion of the novel loss function with a customized lesion weight, as proposed in this paper, is added to No.4. Consequently, these additions contribute to the enhancement of the AUPR metric, resulting in No.5. This demonstrates the effectiveness of the novel loss function in improving the segmentation results. To investigate the impact of different loss functions on the algorithm's performance and demonstrate the effectiveness of the proposed loss function, we performed additional ablation experiments by varying the settings of the loss functions. Based on No.4, the loss function is replaced by different loss functions in Section 3.4, and the results are compared. The results are shown in Table 5.  
According to Table 5, replacing the loss function in the No.4 model with other loss functions, similar to No.6 utilizing the Tversky loss and No.7 employing BCE loss, did not lead to effective improvements. Although, No.6 exhibited a slight improvement in the mAUPR compared with No.4, and the segmentation results for SE lesion types decreased from 45.41% to 43.91%. Furthermore, No.7 experienced a 6.7% decrease in the mAUPR compared to No.4. In addition, by changing the loss function to a combination of Dice loss and BCE loss (No.8), there was a significant drop in performance.