HPPE ‐Unet: A Boundary‐Enhanced U‐Shaped Network With Hybrid Attention and Multi‐Scale Feature Fusion for Small‐Sample Medical Image Segmentation

ABSTRACT Accurate diagnosis of diseases relies on the subjective experience of physicians. Automatic image segmentation technology can assist doctors in quickly locating lesion areas, thereby improving diagnostic efficiency. In the field of medical image segmentation, the current mainstream models are mostly variants of Swin‐Unet, which perform well on large‐sample datasets. However, due to the window constraints of the Swin‐Transformer architecture, these models often face performance limitations when processing small‐sample datasets. In contrast, convolutional‐based Unet variant models exhibit better segmentation performance under small‐sample conditions but demonstrate poorer segmentation results for medical images with blurred boundaries. To tackle these challenges, this paper introduces a novel boundary information‐enhanced architecture, the Hybrid‐Parallel‐Attention and Pyramid‐Edge‐Extraction Enhanced UNet (HPPE‐Unet). Based on an encoder‐decoder architecture of convolutional neural networks, the model incorporates a multi‐scale feature extraction strategy and an optimized skip‐connection mechanism. It integrates two key modules: the Pyramid Edge Extraction (PEE) module and the Hybrid Parallel Attention (HP) module. The PEE module is applied at each stage of the encoder and decoder, significantly enhancing the model's ability to capture subtle boundary structures through multi‐scale feature fusion and boundary reinforcement mechanisms. The HP employs a residual parallel structure combining spatial (SA) and channel attention (CA) blocks, effectively bridging the semantic gap between features in the encoding and decoding stages and addressing the issue of information loss in skip connections between the encoder and decoder. The proposed method was evaluated on an aortic dissection dataset provided by a tertiary hospital. The results show that the method achieved a Dice similarity coefficient (DSC) of 97.65% and a Mean intersection over union (Miou) of 97.92% in the segmentation task. On the public Automated Cardiac Diagnostic Challenge (ACDC) dataset, the DSC reached 90.39%. These results demonstrate that the proposed method holds significant practical value for clinical disease diagnosis.