Local-global multi-scale attention network for medical image segmentation.

ABSTRACT: With the continuous advancement of deep learning technologies, deep learning-based medical image segmentation methods have achieved remarkable results. However, existing segmentation approaches still face several key challenges, including the insufficient extraction of local and global information from images and the inaccurate selection of core features. To address these challenges, this article proposes a novel medical image segmentation architecture-local-global multi-scale attention network (LGMANet). LGMANet introduces an innovative local-global information processing block (LGIPB) to effectively facilitate the deep mining of both local and global information during the downsampling process. In addition, an efficient multi-scale reconstruction attention (EMRA) module is designed to he

SUBMITTER: Zhu M 

PROVIDER: S-EPMC12453874 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature