## 2 Overall Design of the Model
### Multi-Stage Information Fusion Strategy  
MobileNetV3 serves as the backbone network, leveraging the comprehensive feature information extracted in its fourth stage. Given the complexity and diversity of pathology images, it is essential to extract both texture and color features in the early stages and deeper semantic features in the later stages. To address this, a multi-stage information fusion strategy is proposed in the present study. The MobileNetV3 network structure divides feature extraction into four stages. Figure 2 reveals that the first stage consists of one convolutional layer and three bottleneck layers (bnecks) with a 3 \(\times\) 3 kernel size. The second stage comprises three bottleneck layers with a 5 \(\times\) 5 kernel size. The third stage includes six bottleneck layers with a 3 \(\times\) 3 kernel size, and the fourth stage consists of three bottleneck layers with a 5 \(\times\) 5 kernel size and one convolutional layer. The multi-stage information fusion strategy processes the input feature maps from all four stages (\(x1\), \(x2\), \(x3\), \(x4\)) and calculates an attention map, which is then multiplied with the fourth stage's feature map to produce the final output. This attention map, a matrix matching the input feature map in dimension, highlights the importance of each pixel. When multiplied pixel by pixel with the fourth feature map, it results in a new feature map that combines the original features with additional important information, thereby enhancing the model's interpretation of breast cancer histopathology images. The fusion process involves several steps, depicted in Figure 2. Initially, a global average pooling (GAP) operation reduces the spatial dimensions of each input feature map to one. These pooled features from all the four stages are concatenated along the channel dimension, and a 1 \(\times\) 1 convolution reduces the number of channels. After applying the ReLU activation function to process  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Input** & **Operation** & **SE Modular** & **Activation Function** & **Step Length** \\ \hline \(224\times 224\times 3\) & conv2d & & HS & 2 \\ \(112\times 112\times 16\) & bneck, \(3\times 3\) & & RE & 1 \\ \(112\times 112\times 16\) & bneck, \(3\times 3\) & & RE & 2 \\ \(56\times 56\times 24\) & bneck, \(3\times 3\) & & RE & 1 \\ \(56\times 56\times 24\) & bneck, \(5\times 5\) & \(\surd\) & RE & 2 \\ \(28\times 28\times 40\) & bneck, \(5\times 5\) & \(\surd\) & RE & 1 \\ \(28\times 28\times 40\) & bneck, \(5\times 5\) & \(\surd\) & RE & 1 \\ \(28\times 28\times 40\) & bneck, \(3\times 3\) & & HS & 2 \\ \(14\times 14\times 80\) & bneck, \(3\times 3\) & & HS & 1 \\ \(14\times 14\times 80\) & bneck, \(3\times 3\) & & HS & 1 \\ \(14\times 14\times 80\) & bneck, \(3\times 3\) & & HS & 1 \\ \(14\times 14\times 80\) & bneck, \(3\times 3\) & \(\surd\) & HS & 1 \\ \(14\times 14\times 112\) & bneck, \(3\times 3\) & \(\surd\) & HS & 1 \\ \(14\times 14\times 112\) & bneck, \(5\times 5\) & \(\surd\) & HS & 2 \\ \(7\times 7\times 160\) & bneck, \(5\times 5\) & \(\surd\) & HS & 1 \\ \(7\times 7\times 160\) & bneck, \(5\times 5\) & \(\surd\) & HS & 1 \\ \(7\times 7\times 160\) & conv2d, \(1\times 1\) & - & HS & 1 \\ \(7\times 7\times 960\) & pool, \(7\times 7\) & - & - & 1 \\ \(1\times 1\times 960\) & conv2d, \(1\times 1\), NBN & - & HS & 1 \\ \(1\times 1\times 1280\) & conv2d, \(1\times 1\), NBN & & - & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: MobileNetV3-Large parameter setting.  
these features, another 1 \(\times\) 1 convolution restores the original number of channels. Finally, sigmoid activation generates the weighted features, which are multiplied with the fourth input feature map to obtain the final weighted feature information. Through this multi-stage processing, the model captures distinct features at various levels: the first two stages focus on texture, color, and morphology information, while the last two stages emphasize the deeper semantic features of the breast cancer histopathology images.