## 4 Experiment
### Ablation Study  
To ascertain the efficacy of image preprocessing, using multi-scale pooling (MSpool), coordinate attention (CA), multi-resolution hierarchical dense convolution (MRHDC), and multi-objective loss (Mloss) techniques, we conducted relevant ablation experiments. Utilizing a publicly available intracranial hemorrhage dataset, we ensured the reliability of the ablation study by employing the top-performing network parameters from the validation set for the final test. The highest values among the test results were used as the definitive experimental outcomes. Table 1 displays the experimental results, highlighting the best outcomes in bold. The integration of MSpool, CA, MRHDC, and Mloss individually enhanced the network's segmentation capabilities, with MRHDC exhibiting the most significant improvement (0.044), followed by Mloss and MSpool. However, the inclusion of MSpool did not yield the same enhancement as MRHDC, suggesting that the network's encoding ability may be inadequate for capturing more useful information. By enlarging the receptive field, the network can learn more valuable information, yielding superior results compared to retaining more data. Specif  
Figure 10: The training and test loss for the proposed method.  
ically, data preprocessing removes redundant features. The multi-objective joint loss function is modified to prioritize focal area characteristics. The coordinate attention mechanism is incorporated to heighten the network's sensitivity to the focal area's location. A novel pooling method is introduced to preserve more effective information during the downsampling process. Finally, the MRHDC module is added to enhance multi-scale information extraction, resulting in progressively improved segmentation accuracy.  
Upon determining the loss function, the impact of pertinent parameters in the MRHDC module was assessed in the experiment. To extract more information, the MRHDC module was positioned at the lowest layer of the U-Net network instead of all layers, mitigating the increase in training difficulty due to network complexity. Tables 2 and 3 demonstrate the effect of the MRHDC module on model performance. Three grouping methods (Group 1, Group 2, and Group 3) were tested, with Group 3 exhibiting the most effective segmentation. However, as the module is added to the bottom layer with the highest number of channels, continuing to expand the number of groups would result in an explosion of network parameters without any specific economic effect. Consequently, three groups were deemed optimal. For the dilation rate of the dilated convolution module, three different dilation rates and three groups were set. As seen in Table 3, the combination of dilation rates 1, 2, and 5 achieved the best segmentation. This combination provided a larger receptive field, smaller shallow feature dilation rate, and a larger deep feature dilation rate, better fulfilling the purpose of multi-scale feature extraction.  
To determine the optimal ratio of two loss functions in the multi-objective joint loss function, we performed ablation experiments by assigning different weights to train the network. Table 4 reveals that when the ratio is 2:8, the Dice segmentation accuracy is maximized. This outcome also serves as the basis for subsequent related experiments.