文献类型：期刊文献

英文题名：HiPerformer: A High-Performance Global–Local Segmentation Model with Modular Hierarchical Fusion Strategy

作者：Tan, Dayu[1]; Xu, Zhenpeng[1]; Su, Yansen[1]; Peng, Xin[2]; Zheng, Chunhou[1]; Zhong, Weimin[2]

机构：[1] Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, 230601, China; [2] Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China

年份：2025

外文期刊名：arXiv

收录：EI(收录号：20250467297)

语种：英文

外文关键词：Medical image processing - Semantics - Signal encoding

摘要：Both local details and global context are crucial in medical image segmentation, and effectively integrating them is essential for achieving high accuracy. However, existing mainstream methods based on CNN-Transformer hybrid architectures typically employ simple feature fusion techniques such as serial stacking, endpoint concatenation, or pointwise addition, which struggle to address the inconsistencies between features and are prone to information conflict and loss. To address the aforementioned challenges, we innovatively propose HiPerformer. The encoder of HiPerformer employs a novel modular hierarchical architecture that dynamically fuses multi-source features in parallel, enabling layer-wise deep integration of heterogeneous information. The modular hierarchical design not only retains the independent modeling capability of each branch in the encoder, but also ensures sufficient information transfer between layers, effectively avoiding the degradation of features and information loss that come with traditional stacking methods. Furthermore, we design a Local-Global Feature Fusion (LGFF) module to achieve precise and efficient integration of local details and global semantic information, effectively alleviating the feature inconsistency problem and resulting in a more comprehensive feature representation. To further enhance multi-scale feature representation capabilities and suppress noise interference, we also propose a Progressive Pyramid Aggregation (PPA) module to replace traditional skip connections. Experiments on eleven public datasets demonstrate that the proposed method outperforms existing segmentation techniques, demonstrating higher segmentation accuracy and robustness. The code is available at https://github.com/xzphappy/HiPerformer. Copyright ? 2025, The Authors. All rights reserved.