Pathology Image Segmentation Network Based on Multiscale Convolution and Attention Mechanism

Deep learning plays an essential role in the segmentation of pathological images. However, most existing deep learning methods still face challenges such as poor segmentation performance and generalization ability on multi-scale pathological image segmentation tasks. To address these issues, we propose a pathological image segmentation network based on multi-scale convolution and attention mechanisms. We design a multi-scale convolution attention module to extract different scales of features and spatially capture global contextual correlation information, effectively filtering redundant noise information and improving the network's generalization ability in handling multi-scale pathological image data. Additionally, we design a multi-scale feature fusion module to integrate features from different scales, enhancing the edge and fine-grained information in the feature maps and improving segmentation results. The experiments were performed on the GlaS, MoNuSeg and Lizard datasets, and the experimental results show that the Dice scores of the proposed method were 91.07%、81.00% and 79.87%, respectively, and the IoU scores were 84.13%、68.22% and 67.26%, respectively. This demonstrates that the proposed method can effectively segment pathology image, improve the segmentation accuracy, and provide a reliable basis for clinical diagnosis.