HM-Net: Hybrid multi-scale cross-order fusion network for medical image segmentation

U-shaped structures are widely employed in medical image segmentation. However, in existing methods, the skip connection component primarily employs straightforward addition or concatenation, which can result in a reduced complementarity between features at hierarchical levels. These approaches can result in problems like imprecise identification of organs and unclear boundaries. In this paper, we propose a Hybrid Multi- scale Cross-order Fusion Network (HM-Net) for medical image segmentation tasks. Specifically, we first design a hybrid pyramid attention module (HPAM) to adaptively deepen shallow semantic features from both the spatial and channel dimensions through multi-scale feature fusion to alleviate the semantic interval between the decoder and encoder in the skip connection. In addition, we propose a cross-order multi-scale fusion decoder, which effectively captures the layered features produced by the decoder for fusion, mitigating information loss during the up-sampling process using a feature enhancement module and substantially improving the edge blurring problem. Through extensive experimentation on both the Synapse and ACDC datasets, our method has demonstrated superior performance compared to previous state-of-the-art methods.