## 4 Results
### Results on BraTS Dataset  
Segmentation results in terms of average Dice score (Equation (1)), Recall (Equation (2)) and Precision (Equation (3)) on the test set for each tumor subregion are reported in Table 1. Our multi-encoder architecture showed strong segmentation performance overall, with some variability across the different subregions. The highest Dice score of 0.88 \(\pm\) 0.15 was achieved on ET, followed closely by 0.86 \(\pm\) 0.14 on ED, while NCR segmentation, which represents the most challenging task, yielded a score of 0.78 \(\pm\) 0.28.  
This internal variation reflects the common difficulties in exactly defining small or irregular structures such as necrosis, which are often less distinct and more variable across patients. On the other hand, the modality-specific encoding and SE-based attention mechanism led to both high precision (0.89 \(\pm\) 0.12) and high recall (0.89 \(\pm\) 0.15) for the ET region, which is better visualized in T1 CE. The ED region also showed balanced performance on precision (0.87 \(\pm\) 0.15) and recall (0.87 \(\pm\) 0.14), highlighting the network's ability to capture larger and more diffuse areas of abnormality.  
NCR segmentation also exhibited slightly lower recall (0.79 \(\pm\) 0.27) compared to ED and ET. Nonetheless, the model showed high precision (0.84 \(\pm\) 0.24), suggesting reliable identification of necrotic tissue when predicted.  
To evaluate the effectiveness of our results and ensure a robust comparative analysis, we trained and tested a SegFormer3D model [18]. This state-of-the-art transformer-based architecture provides a good tradeoff between efficacy and model complexity, making it a solid benchmark in the context of brain tumor segmentation. All experiments were carried out under the same experimental conditions to provide consistency, including the same dataset splits, training parameters, 5-fold cross-validation strategy, evaluation metrics, and computational environment. As shown in Table 1, the multi-encoder model consistently outperformed the SegFormer3D baseline across all metrics and classes.  
In terms of Dice scores, the best SegFormer3D performance was observed for ET (0.79 \(\pm\) 0.18), similar to the multi-encoder, followed by ED (0.78 \(\pm\) 0.18), while NCR was confirmed to be the most challenging region, with the lowest Dice score of 0.66 \(\pm\) 0.29.  
The precision and recall metrics reflected this trend as well, with ET achieving relatively high precision and recall of 0.80 \(\pm\) 0.17 and 0.80 \(\pm\) 0.19, respectively, indicating stable and consistent predictions. For ED, the model reached a precision of 0.77 \(\pm\) 0.18 and recall of 0.82 \(\pm\) 0.18, suggesting a trend of oversegmentation, whereas for NCR the precision (0.68 \(\pm\) 0.28) and recall (0.74 \(\pm\) 0.26) were both lower, showing both under- and oversegmentation tendencies.  
The statistical analysis conducted on the models found significant differences on all tumor subregions and all metrics (\(p\) < 0.001). To provide a comprehensive evaluation of our model, performance metrics on tumor core (TC) and whole tumor (WT) composite regions are also reported. In both cases, the improvements on all metrics were significant compared to SegFormer3D.