DBE-SwinUNet: A Novel Dual-Branch SwinUNet for Accurate Cancer Pathology Image Segmentation

In the field of medical image analysis, precise cancer pathology image segmentation is crucial for assisting clinicians in identifying lesion areas. This paper proposes an innovative dual-branch architecture, DBE-SwinUNet, to overcome the reliance on expert knowledge, inefficiencies, and the limitations of local feature extraction in traditional deep learning models. We introduced depthwise separable convolution (DSConv) to enhance global feature representation and integrated Multi-Scale Dense Skip Connections (MSDSC) to facilitate information flow between the encoder and decoder, improving model stability and segmentation accuracy. Additionally, we incorporated a convolutional neural network enhancement module (DA-DWConv) with a dual attention mechanism, which enhances local feature extraction while effectively controlling model complexity. Experimental results show that on the gastric cancer pathology image (GCPI) dataset, our method achieved a Dice similarity coefficient (DSC) of 90.15 % and a 95th percentile Hausdorff distance (HD95) of 39.25 mm. On the public Synapse dataset, the method reached 80.42% DSC and 19.28 mm HD95, significantly outperforming existing methods.