A dual-branch network for lesion segmentation in medical images using state space models.

ABSTRACT:

Background

Lesion segmentation in medical images is crucial for clinical diagnosis and treatment planning. However, existing methods often struggle to effectively extract both local and global features, limiting segmentation accuracy. To address this challenge, we propose a dual-branch network that integrates state space models (SSMs) with deep convolutional networks to enhance the extraction of both local and global features, thus improving lesion segmentation performance.

Methods

The proposed model employs a dual-branch encoder: one branch incorporates the visual state space encoder to efficiently model long-range contextual dependencies, while the other branch, based on the residual network, extracts hierarchical local features. To refine feature representation, we introd

SUBMITTER: Chen H 

PROVIDER: S-EPMC12682491 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature