H2MaT-Unet:Hierarchical hybrid multi-axis transformer based Unet for medical image segmentation

Accurate segmentation and lesion localization are essential for treating diseases in medical images. Despite deep learning methods enhancing segmentation, they still have limitations due to convolutional neural networks' inability to capture long-range feature dependencies. The self-attention mechanism in Transformers addresses this drawback, but high-resolution images present computational complexity. To improve the convolution and Transformer, we suggest a hierarchical hybrid multiaxial attention mechanism called H2MaT-Unet. This approach combines hierarchical post-feature data and applies the multiaxial attention mechanism to the feature interactions. This design facilitates efficient local and global interactions. Furthermore, we introduce a Spatial and Channel Reconstruction Convolution (ScConv) module to enhance feature aggregation. The paper introduces the H2MaT-UNet model which achieves 87.74% Dice in the multi-target segmentation task and 87.88% IOU in the single-target segmentation task, surpassing current popular models and accomplish a new SOTA. H2MaT-UNet synthesizes multi-scale feature information during the layering stage and utilizes a multi-axis attention mechanism to amplify global information interactions in an innovative manner. This re-search holds value for the practical application of deep learning in clinical settings. It allows healthcare providers to analyze segmented details of medical images more quickly and accurately.