Abstract: The proposed model leverages frequency-domain information extracted via Fourier transform as an optimization basis,enhancing the network’s ability to identify camouflaged lesion regions under highly similar backgrounds.By designing the Frequency Feature Enhancement Module (FFEM),the network can significantly strengthen lesion-related features across different frequencies,thereby enabling more precise capture of subtle camouflaged patterns in complex contexts.In addition,a novel strategy integrates frequency-domain prior maps into the loss function through weighted fusion,guiding the optimization process to focus on lesion features and improving the network’s sensitivity and adaptability during training.Furthermore,a Cross-Attention Fusion Module(CAFM) is designed to perform differentiated enhancement of multi-frequency features,further enhancing the network’s ability to regulate and balance frequency-specific information.The proposed method demonstrates outstanding segmentation performance across multiple medical imaging datasets(skin datasets ISIC 2016,ISIC 2017,ISIC 2018;colon polyp datasets CVC-Clinic,Kvasir,CVC-ColonDB,ETIS-LaribPolyDB;breast dataset BUSI).In quantitative evaluations,including Dice coefficient,Intersection over Union(IoU),and Accuracy(ACC),the method outperforms existing models,achieving superior accuracy and robustness.

Key words: Medical image segmentation, Frequency prior, Feature enhancement, Camouflaged lesion segmentation, Attention