## 2 Materials and Methods
### Deep Learning Framework  
In this study, we developed a multi-modal image classification network based on the 3D ResNet18 architecture (Figure 2). The primary function of 3D ResNet18 in our framework is the extraction of features from T1- and T2-weighted MRI images. We chose 3D ResNet18 for its inherent capability to handle three-dimensional data, crucial for accurately interpreting the complex structures within MRI images. Unlike other models primarily designed for 2D image processing, 3D ResNet18 employs 3D convolutional kernels, effectively capturing spatial details across all dimensions. This feature makes it particularly suitable for medical imaging applications where precision is of the utmost importance. To enhance the model's feature extraction capabilities, we strategically incorporated spatial and channel attention modules within different residual blocks of the 3D ResNet18 network [28]. Concurrently, CentralNet, proven to be effective in integrating features from different modalities, as demonstrated by [PERSON] et al. [24], is deployed between the two feature extraction pathways to integrate stage-specific features from T1 and T2 modalities. This architecture offers a more dynamic and effective feature integration compared to traditional feature fusion methods. The multi-layered approach of CentralNet allows for a richer and more nuanced representation of data, making it an optimal choice for the complex requirements of medical image analysis. This design leads to a robust model, fine-tuned through joint multi-task training, and specifically aimed at the classification of ABE. This method represents a harmonious integration of advanced attention mechanisms with multi-modal feature fusion, significantly boosting the model's diagnostic accuracy. In our methodology, attention modules are integrated within the residual blocks of the 3D Res-Net18 network (Figures 3 and 4) [28]. These modules enhance the model's focus across spatial and channel dimensions, substantially enhancing its feature extraction capacity. The spatial attention module uses maximum and average pooling operations to generate spatial weight maps, directing the model's attention towards crucial image regions for classification. The channel attention module computes maximum and average  
Figure 2: The proposed deep learning framework for ABE prediction, leveraging a multi-modal feature fusion approach. The architecture employs dual 3D ResNet18 networks for parallel feature extraction from T1- and T2-weighted MRI images. The input for the model is a 3D construct composed of a sequence of six adjacent slices centered around the GP region from the MRI scans. A CentralNet module integrates the feature maps derived from both T1- and T2-weighted images, ensuring comprehensive feature representation. The model is subsequently trained, utilizing a joint multi-task learning strategy aimed at enhancing the predictive accuracy for ABE. pooling along the channel axis, producing two sets of shared weight coefficients for the channels. These coefficients are processed through a shared-weight multi-layer perceptron (MLP) and subsequently aggregated via weighted summation to derive unique channel weight coefficients. This dual attention mechanism ensures balanced and prioritized comprehension of spatial and channel-specific features, essential for high precision in classification tasks. CentralNet's architecture integrates features from different modalities by taking the weighted sum of layers from the corresponding unimodal networks and its own preceding layers as input. Each modality is processed by a distinct deep convolutional network, with a central network integrating these modality-specific features. Traditional methods of multi-modal feature fusion involve independently extracting features from each modality and then combining them. In contrast, the CentralNet structure enables mutual supervision and learning from each other during the feature extraction process across different modalities.