## 4 Experiments and Results
### Experimental Results and Analysis  
#### 4.5.1 Comparison of Evaluation Metrics' Results on DDR Dataset  
In this paper, MLNet is compared with seven state-of-the-art DR lesion segmentation methods, including M2 MRF [22], L-Seg [37], RTNet [9], HEDNet [38], Swin Transformer [39], VTA [20], and [PERSON] et al. [11]. Table 1 presents the evaluation metrics and mean evaluation metrics' results of MLNet and the seven advanced methods on the DDR dataset. According to Table 1, MLNet outperforms other advanced methods in all average evaluation metrics, showcasing its superior segmentation performance. The improvements achieved by MLNet compared to the second-best results are notable, with an enhancement of 1.87% in mAUPR, 4.28% in mDice, and 6.92% in mIoU. These results highlight MLNet's ability to capture fine-grained lesion details and generate precise segmentations. Furthermore, according to Table 1, a comparison with M2 MRF reveals substantial improvements achieved by MLNet in the MA segmentation results. Specifically, MLNet shows improvements of 6.07%, 3.44%, and 13.66% in AUPR, Dice, and IoU, respectively, emphasizing its effectiveness in accurately segmenting MA lesions. Similarly, for the EX segmentation results, MLNet demonstrates improvements of 2.99%, 2.06%, and 8.43% in AUPR, Dice, and IoU, respectively, further consolidating its superior performance in capturing and delineating EX lesions. However, for the SE segmentation results, MLNet's AUPR is lower than M2 MRF by 4.78%, but Dice and IoU show improvements of 1.69% and 0.97%, respectively. This observation could be attributed to MLNet's loss function, \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**AUPR/\%**} & \multicolumn{4}{c}{**Dice/\%**} & \multicolumn{4}{c}{**IoU/\%**} \\ \cline{2-13}  & **MA** & **SE** & **HE** & **EX** & **mAUPR** & **MA** & **SE** & **HE** & **EX** & **mDice** & **MA** & **SE** & **HE** & **EX** & **mIoU** \\ \hline M2 MRF [22] & 28.21 & **53.19** & 54.20 & 64.17 & 49.94 & 27.81 & 48.02 & 45.29 & 61.15 & 45.57 & 16.15 & 31.60 & 29.28 & 44.04 & 30.27 \\ L-Seg [37] & 10.52 & 26.48 & 35.86 & 55.46 & 32.08 & 23.01 & 40.13 & 41.53 & 54.44 & 39.78 & 10.13 & 28.01 & 24.23 & 35.24 & 24.40 \\ RTNet [9] & 11.76 & 29.43 & 36.56 & 56.71 & 33.62 & - & - & - & - & - & - & - & - & - \\ HEDNet [38] & 20.61 & 46.68 & 43.19 & 61.40 & 42.97 & 22.43 & 45.50 & 42.61 & 56.63 & 41.79 & 12.63 & 29.46 & 27.09 & 39.50 & 27.17 \\ Swin-T [39] & 23.67 & 46.21 & 54.39 & 62.71 & 46.75 & 23.31 & 46.77 & 50.53 & 59.79 & 45.10 & 13.19 & 30.62 & 33.82 & 42.64 & 30.07 \\ VTA [20] & 10.66 & 26.94 & 37.78 & 56.07 & 32.86 & 23.30 & 41.08 & 42.77 & 55.57 & 40.68 & 13.44 & 30.01 & 25.47 & 36.94 & 26.47 \\ [PERSON] et al. [11] & 11.94 & 32.60 & 36.52 & 60.14 & 35.30 & 15.22 & 38.76 & 33.17 & 54.06 & 34.80 & - & - & - & - & - \\ MLNet & **34.28** & 48.41 & **57.39** & **67.16** & **51.81** & **31.25** & **49.71** & **55.23** & **63.21** & **49.85** & **29.81** & **32.57** & **33.91** & **52.47** & **37.19** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation metrics of different methods on the DDR dataset. which considers all four lesion types simultaneously. While this may lead to a potential decrease in the segmentation capability for a specific lesion type, the overall evaluation metrics remain excellent, indicating MLNet's robust performance in segmenting DR lesions. Compared to Swin Transformer, which has the second-best evaluation metrics for the HE segmentation results, MLNet achieves improvements of 3%, 4.7%, and 0.09% in AUPR, Dice, and IoU, respectively. These advancements underscore MLNet's effectiveness in accurately segmenting HE lesions, further solidifying its superiority over other methods. Overall, MLNet achieves the best average evaluation metrics and significant improvements compared to the second-best results. It also achieves the best results in Dice and IoU in non-average evaluation metrics and mostly achieves the highest scores in AUPR.