RSFormer: medical image segmentation based on dual model channel merging

Image segmentation technology is constantly advancing, especially in its application in medical diagnosis and treatment. Many segmentation tasks are based on the U-Net structural model method in convolutional neural networks, but this structure constrains feature extraction, and its global modeling ability still needs to be improved. Specifically, traditional convolution operations cannot capture feature information at different scales, leading to limited localization of local detail features and lower precision in global feature extraction. Based on these issues, we propose a new structure called RSFormer. The RSFormer architecture combines the Transformer's ability to extract crucial segmentation features in the main branch with a supplementary fully convolutional branch to address its limitations in full-size prediction, thereby enhancing its overall performance and applicability. By fusing the result features of the two branches, we can ultimately predict the segmentation map of <i>h&times;w</i>. Our method demonstrates its performance in terms of mDice, mIoU, and mPrecision metrics on three datasets benchmarks. These results indicate that the proposed RSFormer model has superior performance on multiple datasets.