AMPPNet : A Lightweight 3D Medical Image Segmentation Network With Dual‐Bond Efficient Hybrid Attention Mechanism

ABSTRACT

Recent medical image segmentation networks, such as SwinUNet and AttentionUNet, achieve high accuracy through complex architectures containing hundreds of millions of parameters. However, due to expensive annotation and limited data (e.g., the MSD‐Pancreas dataset contains only 281 samples), these models often suffer from overfitting and poor generalization. To address these challenges, we propose a lightweight 3D medical image segmentation network, AMPPNet, with only 1.97 M parameters and three major innovations: the Adaptive Multi‐stage Polarization Perception (AMPP) module, which integrates two key submodules: dual‐bond efficient polarization multi‐scale channel self‐attention (DPMC) and dual‐bond multi‐scale polarization hybrid spatial attention (MSPHA). The DPMC module achieves cross‐layer channel semantic alignment and local–global polarization fusion through a dual‐bond design, while the MSPHA module captures multi‐scale spatial dependencies by jointly attending to local texture details and global structural cues. These modules, combined with the Adaptive Multi‐stage Feature Fusion (AMFF) mechanism, enable hierarchical semantic aggregation with minimal computational overhead. The Pyramid Adaptive Matrix Multi‐Level Loss (PAMMLoss) dynamically combines Dice, cross‐entropy, and Tversky losses at shallow, mid, and deep layers with learnable adaptive weights, enabling fine‐grained optimization and mitigating gradient conflicts. AMPPNet is highly parameter‐efficient and accurate—despite its compact design, it achieves excellent Dice scores of 81.17% (NIH), MSD (10 datasets) of 72.71%, demonstrating its strong generalization capabilities under data‐scarce conditions. Our code is publicly available: https://github.com/XiangruW/AMPPNet .