SFCI-Mamba: Factorized Amplitude–Phase Modeling with Spatial–Frequency Interaction for Skin Lesion Segmentation

Skin lesion segmentation requires both boundary detail and global structure. We study two questions in spatial–frequency state-space modeling: how to model Fourier amplitude and phase, and how spatial and spectral features should interact before fusion. We present SFCI-Mamba, a compact encoder–decoder built around spatial-frequency collaborative state-space blocks (SFCBs). Each SFCB independently maps amplitude- and phase-domain representations with Mamba, while dual-context interaction (DCI) lets each branch retrieve from a shared spatial-frequency context using its own query before fusion. SFCI-Mamba achieves micro-averaged Dice scores of 0.9121, 0.9001, and 0.9228 on ISIC2017, ISIC2018, and PH${}^2$, respectively. The PH${}^2$ result is obtained using the ISIC2018-trained model without fine-tuning. Among the tested variants, frequency modeling outperforms spatial replacements, joint amplitude–phase modeling gives the strongest overlap, and DCI improves over the tested interaction alternatives. These results support factorized spectral state-space modeling with pre-fusion spatial–frequency interaction for dermoscopic lesion segmentation.