error correction2 Effectiveness of multi-scale feature fusionMulti-scale feature fusion makes full use of mul-ti-dimensional feature information which can reduce the loss of information Therefore in o

Error correction: 2) Effectiveness of multi-scale feature fusion Multi-scale feature fusion makes full use of multidimensional feature information, which can reduce information loss. Therefore, in order to verify the impact of adding a multi-scale feature fusion module on network performance, training and testing were conducted on dataset #1 and dataset #2 with other parameters kept the same. The experimental results are shown in Table V and Table VI, where MSFF represents the multi-scale feature fusion module. From the experimental results, it can be observed that the network performance improves after adding the multi-scale feature fusion module. Additionally, Figure 7 shows some of the visualization test results of the ablation experiments. It can be seen that when CoTNet-50 and multi-scale feature fusion are added simultaneously, the network achieves the best performance and produces better segmentation results