## 4 Experimental Results
### Main Result  
For **BraTS 2019**, the training set of this dataset contains 335 multi-modal MRI cases. A five-fold cross-validation evaluation was performed on the training set. The dice coefficients of our dual-path encoder model in WT, ET, and TC were, respectively, 93.68%, 90.75%, and 89.2%, and the Hausdorff distance 95% (HD) values were 6.54, 7.02, and 4.32, respectively. At the same time, we also compared our model with other SOTA models using the BraTS 2019 validation set, which contains 125 cases without any annotation. We uploaded all data to [[https://ipp.cbica.upenn.edu/](https://ipp.cbica.upenn.edu/)]([https://ipp.cbica.upenn.edu/](https://ipp.cbica.upenn.edu/)) (accessed on 18 January 2024) to verify this model and compare it with other models, and the results are detailed in Table 2. We put forward the model of our dual-path encoder, and the dice coefficients in WT, ET, and TC were 91.42%, 85.63%, and 87.2%, respectively. For HD95, the values were 7.21, 8.32, and 5.64, respectively. We applied the proposed dual-path encoder model (DualTrans) to the BraTS 2019 validation dataset and evaluated the performance online on the Challenge web. Compared with Swin UNETR, our model showed great advantages in both indicators and showed significant improvement. Swin UNETR is a reformulation of the task of 3D semantic segmentation as a sequence-to-sequence prediction problem, and maps multimodal MRI features to one-dimensional sequences as the input to the layered Swin Transformer encoder. The model was verified with BraTS 2021; we obtained the source code of the model and verified it with BraTS 2019 according to the same training details, and the results were obtained as shown in Table 2. Swin UNETR combines the architectures of the Swin Transformer and UNETR (Transformer-based U-Net), utilizing the Swin Transformer to extract global context information. In comparison to Swin UNETR, our proposed DualTrans model achieved improvements in the dice coefficient: by 0.4% in the ET region, 0.3% in the TC region, and 0.42% in the WT region. This indicates that our dual-path encoder model, which leverages CNN to extract local context information, outperforms Swin UNETR, which solely employs the Swin Transformer in the encoder for segmentation tasks. On the other hand, the 3D-UNet model uses convolutional neural networks to extract feature information. Although CNN has unique advantages in extracting local context information, it has limitations in capturing global context information. In comparison to 3D-UNet, our DualTrans model showed significant improvements in the dice coefficient: by 14.77% in the ET region, 14.72% in the TC region, and 4.04% in the WT region. This demonstrates that, by leveraging the Swin Transformer for global context information and a CNN for local context information, and then combining these extracted features for upsampling, our model captures richer semantic features. At the same time, we also show the comparative analysis results of excellent models, such as TransBTS, Attention-based, Cross-Sequence, Two-Stage Cascaded, 3D-UNet, etc. Compared with the TransBTS model, in brain-tumor areas such as ET, TC, and WT, we have achieved an increase of 7.3%, 6.8%, and 2.5%, respectively, in the dice coefficient. The TransBTS model combines a CNN and the Transformer to first use a 3D CNN brain-tumor-volume spatial feature map and then transforms the feature mapping, thus proving