DPMNet : Dual-Path MLP-Based Network for Aneurysm Image Segmentation

MLP–based networks, while being lighter than traditional convolution– and transformer–based networks commonly used in medical image segmentation, often struggle with capturing local structures due to the limitations of fully–connected (FC) layers, making them less ideal for such tasks. To address this issue, we design a Dual−Path MLP–based network (DPMNet) that includes a global and a local branch to understand the input images at different scales. In the two branches, we design an Axial Residual Connection MLP module (ARC–MLP) to combine it with CNNs to capture the input image’s global long–range dependencies and local visual structures simultaneously. Additionally, we propose a Shifted Channel–Mixer MLP block (SCM–MLP) across width and height as a key component of ARC–MLP to mix information from different spatial locations and channels. Extensive experiments demonstrate that the DPMNet significantly outperforms seven state–of–the–art convolution– , transformer–, and MLP–based methods in both Dice and IoU scores, where the Dice and IoU scores for the IAS–L dataset are 88.98% and 80.31% respectively. Code is available at https://github.com/zx123868/DPMNet .