Work

DBFA-Net: Diffusion-Based Bidirectional Fusion and Axial Attention Network for medical image segmentation

Year: 2026

Type: article

Abstract: In recent years, Denoising Diffusion Probabilistic Models (DDPMs) have shown strong modeling capability and are increasingly applied to medical image segmentation tasks. However, existing diffusion models still struggle with fine-grained structures and multi-scale context, particularly with semantic–noise feature fusion and global dependency modeling. To address these challenges, this paper proposes a Diffusion-Based Bidirectional Fusion and Axial Attention Network (DBFA-Net). Specifically, we design a Bidirectional Feature Fusion Module (BiFFM) that facilitates deep fusion of semantic and noise features through a bidirectional guidance mechanism. Additionally, we introduce a Frequency-domain Axial Cross-Attention Module (FACAM), which captures cross-scale global dependencies via axial attention in the frequency domain, enhancing long-range dependency modeling. Moreover, we introduce a Multi-Scale Axial Aggregation Attention Module (MSA3M) to aggregate multi-scale spatial information during the diffusion decoding process, enhancing the network’s ability to capture fine-grained structures. We conducted experiments on three public datasets to evaluate the effectiveness of DBFA-Net and compared it with existing methods. The results show that DBFA-Net achieves competitive or superior segmentation performance compared with most of the evaluated methods under the adopted experimental settings.

Source: Biomedical Signal Processing and Control

DOI: 10.1016/j.bspc.2026.111620