## 4 Results
### Performance on the ISLES 2022 Dataset  
The images of the ISLES 2022 dataset were acquired in multiple MRI centers with different equipment, making it challenging to capture the underlying trends in the data. Moreover, the distribution of the lesion size presents a high variance, as observed in Figure 2b. The results in the different planes for the DWI and ADC sequences provided in this dataset are presented in Table 6. For this dataset, the proposed model failed to segment many images. However, the segmentation on the DWI images reached an IoU of 0.6961 in the axial plane, although a mean of 121 images could not be segmented. The experiments with the ADC sequence resulted in the worst results, with a mean IoU of 0.5004 for the three planes. Figure 10: Examples of segmented FLAIR images in the sagittal plane by the proposed method (second row) and their corresponding ground truth mask (first row) for mask categories Small (**a**,**e**), Medium Down (**b**,**f**), Medium Up (**c**,**g**), and Large (**d**,**h**). Figure 9: Examples of segmented FLAIR images in the coronal plane by the proposed method (second row) and their corresponding ground truth mask (first row) for mask categories Small (**a**,**e**), Medium Down (**b**,**f**), Medium Up (**c**,**g**), and Large (**d**,**h**). The F1-Scores of the model using the best configuration for the ISLES 2015 dataset were compared against the best configuration for ISLES 2022 to explore the proposed model's performance when changing the key hyperparameters. For notation convenience, the configuration using (\(\lambda_{GDL}=0.3\), \(\lambda_{FL}=0.7\)) is named as configuration A, and the one using (\(\lambda_{GDL}=0.1\), \(\lambda_{FL}=0.9\)) is noted as configuration B. The results are displayed in Figure 11.  
It is noted that the median F1-Scores in the different mask size categories surpass 0.8950 for DWI using both hyperparameters' configurations, where (\(\lambda_{GDL}=0.1\), \(\lambda_{FL}=0.9\)) has higher median values. The general median values of configurations A and B were 0.9125 and 0.9198, respectively. Nonetheless, their mean values are diminished due to the non-segmented images in both configurations. Furthermore, it can be observed that the mean value of DWI's Small category is lower in the (\(\lambda_{GDL}=0.1\), \(\lambda_{FL}=0.9\)) configuration than the mean resulting from configuration A. This is because the first one resulted in a more elevated number of N. S. (121 images), while the second registered a mean of 115. A violin plot of the mask size of non-segmented images by each configuration applied to DWI modality is provided in Figure 12. The mean value in the plots shows that although the (\(\lambda_{GDL}=0.3\), \(\lambda_{FL}=0.7\)) configuration results in a lower number of N. S. images, it struggles to segment lesions with a higher number of pixels than the non-segmented for the model using (\(\lambda_{GDL}=0.1\), \(\lambda_{FL}=0.9\)). Consequently, the last configuration performs a more precise small lesion segmentation. A point worth mentioning is that the mask sizes of the N. S. images do not surpass 300 pixels, corresponding to a smaller size than the median in the lesions registered in ISLES 2015. Independently of the hyperparameters configuration, applying the proposed method on ADC images produces many non-segmented images with small lesions. Moreover, the median F1-Score was set at 0.5366 with configuration A and 0.5874 with configuration B, demonstrating that the model performs poorly using this MRI sequence compared to the DWI modality. However, the general median using configurations A and B on ADC images was registered at 0.8180 and 0.8350, respectively, assessing a better performance in larger lesions than in small ones. These results could be due to the lesion's visibility in both MRI modalities. While DWI measures the water diffusion in the tissue, the ADC image quantifies the magnitude of the water diffusion.