# ChroSegNet: An Attention-Based Model for Chromosome Segmentation with Enhanced Processing
## 3 Method
### Network Architecture  
ChroSegNet is designed based on U-Net [23] as we reviewed in Section 2. On the one hand, compared with U-Net, in order to take into account the segmentation of small chromosomes, we constructed more subsampling layers and convolutional layers to expand the receptive field. On the other hand, the traditional attention-based U-Net only pays attention to the feature information of a certain dimension (for example, attention gate [33] only pays attention to the spatial dimension. ), which is easy to cause the feature information obtained is not comprehensive enough, especially when segmenting images with complex feature information such as electron microscopic images. In contrast, the hybrid attention module we designed focuses on both channel and spatial feature information. The network structure of ChroSegNet is shown in Figure 3.  
ChroSegNet is an encoder-decoder structure. The encoding part is a backbone feature extraction network, which is mainly composed of the convolution layers consisted of 3 \(\times\) 3 convolution kernels, 2 \(\times\) 2 maximum pooling layer, and ReLU activation function. Instead of directly adding attention modules to the original network, we designed more subsampling layers and convolutional layers for ChroSegNet (the newly added parts are shown in bold) to further expand the receptive field and integrate more comprehensive multiscale feature information. The decoding part is an enhanced feature extraction network, which consists of jump connections, upsampling layers, convolution layers, and hybrid attention modules. The hybrid attention modules are incorporated at the end of the skip connections in layers 2, 3, and 4 to generate the multiscale attention information. The attention  
Figure 3: ChroSegNet network structure diagram. information is then input into the feature fusion layer to promote the combination of the high-dimensional key features and the low-dimensional features. This would help the network focuses on more meaningful target regions and suppresses the activation values of the background and the irrelevant regions. The hybrid attention module are mainly responsible for extracting the key parts of the high-dimensional features. However, the first layer of the skip link only contains shallow features and does not involve the information fusion of the high and the low dimensions. Hence, the hybrid attention module is not incorporated in this layer. The structure of the hybrid attention module is shown in Figure 4, where \(S\) represents the feature map of the current jump-connected input, \(X\) represents the feature map of the current input, \(X^{\prime}\) represents the output value of the channel attention module, \(X^{\prime\prime}\) represents the output of the hybrid attention module, \(\alpha\) represents the attention coefficient, and \(\beta\) represents the spatial attention coefficient. The feature map of the encoder input through the jump connection layer and the current input feature map of the decoder first enter the channel attention module to extract the channel attention information. The process of the channel attention module is as follows: the average pooling layer and the maximum pooling layer are used to compress the spatial dimension of the input feature mapping. Among them, the average pooling layer is a commonly used means of spatial information aggregation, and the maximum pooling layer has been proved to be able to collect the key clues of the features of different objects [34]. Therefore, we adopt the combination of the two pooling methods to obtain more representative information. Then, the descriptor information after secondary fusion is input into Multilayer Perceptron (MLP) including convolution layer and ReLU layer to obtain channel attention mapping. Finally, Sigmoid is used to change it into the channel attention coefficient (\(\alpha\)) between 0 and 1 and multiply with the current input feature map to obtain the feature map with the channel attention information (\(X^{\prime}\)).