The accurate segmentation of lung tumors plays a crucial role in tumor diagnosis and treatment. However, lung tumor segmentation is often challenged by several issues such as low contrast between lesions and surrounding tissues, tumor-normal tissue adhesion, and high background noise. To address these, this study introduces a lung tumor segmentation method based on Transformer and attention mechanisms. In the Transformer encoder stage, both global and local attention mechanisms are incorporated to enable the network to simultaneously focus on both global and local contextual information. In the skip connection stage, a channel-prior convolutional attention mechanism is utilized to enhance the spatial perception ability for complex lesions and reduce the channel dimension redundancy, such that the tumor segmentation accuracy can be improved. The experimental results on the private GDPH and public LUNG1 datasets demonstrate that the proposed method outperforms eight comparative methods in terms of the Dice metric by achieving approximately 90.96% and 88.18% on the two datasets, respectively. The proposed method can provide reliable assistance for clinical diagnosis and treatment.

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.