SFA-DiffNet: Spatial-frequency aware diffusion network for robust medical image segmentation

Abstract

Diffusion models have demonstrated significant potential in medical image segmentation, yet they remain challenged in capturing complex fine-grained structures. Existing spatial-domain backbones exhibit bias towards low-frequency components, while the denoising process often leads to over-smoothing of high-frequency details, compromising boundary precision and cross-domain generalization. To overcome these limitations, we propose a Spatial-Frequency Aware Diffusion Network (SFA-DiffNet) that jointly models spatial and frequency representations, augmented with conditional guidance from foundation models. This design improves detail preservation and boundary accuracy in our experiments. Specif..