SAM2-FNet: A Frequency-Domain Mul-ti-Expert Fusion Network for Medical Image Segmentation

In recent years, deep learning has achieved substantial progress in medical image segmentation; however, existing methods remain constrained by the challenge of effectively coupling local detail with global semantic context. To address this issue, we propose SAM2-FNet, a frequency-domain expert-fusion network built upon an improved SAM2-UNet. SAM2-FNet attains fine-grained, multi-scale feature enhancement through frequency-domain decoupling and a multi-expert collaborative mechanism, leading to marked improvements in segmentation accuracy.Specifically, SAM2-FNet provides three primary capabilities:(1) Frequency-domain decoupling and enhancement— input features are decomposed into high-frequency and low-frequency components via a two-dimensional Fourier transform; the resulting components are processed by a dual-branch heterogeneous pipeline and adaptively fused using channel-wise attention to preserve both texture details and global structure.(2) Dynamic expert decision-making — an improved Expert Fusion Module (FEM) implements a multi-expert architecture composed of five sub-networks (SubNets), enabling dynamic, data-driven expert weighting and enhancing the model’s generalization to lesion heterogeneity.(3) Decoder optimization — a Local Refinement Module (LRM) is introduced in the decoder: an atrous (dilated) convolutional pyramid combined with a spatial-attention mechanism narrows the encoder–decoder semantic gap and improves boundary reconstruction fidelity.Experiments on the Kvasir-SEG dataset demonstrate that SAM2-FNet yields a 3.0% absolute increase in Dice coefficient and a 4.1% absolute increase in IoU relative to the baseline SAM2-UNet. Moreover, compared with benchmark models such as umambabot and SwinUNETR, SAM2-FNet exhibits superior robustness under high-noise and low-contrast imaging conditions