DBFAM: A dual-branch network with efficient feature fusion and attention-enhanced gating for medical image segmentation

Abstract

In the field of medical image segmentation, convolutional neural networks (CNNs) and transformer networks have garnered significant attention due to their unique advantages. However, CNNs have limitations in modeling long-range dependencies, while transformers are constrained by their quadratic computational complexity. Recently, State Space Models (SSMs), exemplified by Mamba, have emerged as a promising approach. These models excel in capturing long-range interactions while maintaining linear computational complexity. This paper proposes a dual-branch parallel network that combines CNNs with Visual State Space Models (VSSMs). The two branches of the encoder separately capture local and global information. To further leverage the intricate relationships between local and global features, a dual-branch local–global feature fusion module is introduced, effectively integrating features from both branches. Additionally, an Attention-Enhanced Gated Module is proposed to replace traditional skip connections, aiming to improve the alignment of information transfer between the encoder and decoder. Extensive experiments on multiple datasets validate the effectiveness of our method.