FARF-Net: Frequency-guided Adaptive Receptive Field Network for Edge-enhanced Polyp Segmentation

Accurate segmentation of colorectal polyps plays a vital role in the early diagnosis and prevention of colorectal cancer. Despite notable progress, existing methods struggle with limited region adaptability due to fixed receptive fields, lack explicit boundary modeling, and are prone to interference from background noise, leading to suboptimal segmentation results. To address these issues, we propose FARF-Net, a novel edge-aware segmentation framework that leverages frequency-domain adaptive receptive fields. Built upon the Pyramid Vision Transformer v2 backbone, FARF-Net introduces three tailored components. Specifically, EdgeKAN module applies Kolmogorov-Arnold Networks for channel-wise nonlinear modeling, enhancing local edge semantics and boundary detail representation. Adaptive Receptive Field module adjusts spatial receptive fields based on localized frequency energy, boosting sensitivity to high-frequency boundaries. Frequency-Guided Dual-Supervision Decoder integrates high-frequency structural features and boundary priors to refine edge predictions and suppress irrelevant high-frequency background noise. Extensive experiments on five public polyp segmentation benchmarks demonstrate that FARF-Net consistently surpasses state-of-the-art methods. Notably, it achieves superior boundary reconstruction and robustness in challenging cases such as blurred contours and small polyps.