Edge-Guided Multi-Scale Frequency Attention Network for Gastrointestinal Cancer Image Segmentation

Image segmentation is a critical technology to improve the accuracy of clinical decisions and treatments in computer-aided diagnostic systems. However, the diverse morphology and fuzzy boundaries of gastrointestinal tumors incur substantial challenges for existing segmentation models, leading to inaccurate feature capture and generating suboptimal results. For solving these problems, we design an edge-guided multi-scale frequency attention network for the gastrointestinal tumor segmentation task, termed EGMFA-Net, which consists of a Kernel Adaptive Enhancement Module (KAEM) and a Frequency-domain Self-attention Module (FDSA). Specifically, KAEM adaptively adjusts the feature extraction kernel based on the morphology of different lesion regions, which enhances the recognition of different morphology regions via a progressive optimization strategy of feature expression. Furthermore, FDSA effectively aggregates multi-scale features in the frequency domain to achieve global receptive fields while preserving more high-frequency details, thereby enhancing adaptability to complex pathological contexts. Extensive experiments on eight medical image benchmark datasets, including SEED, Kvasir, ClinicDB, ColonDB, ETIS, BKAI, CVC-300, and Synapse, show that EGMFA-Net attains state-of-the-art performance over existing methods. Our implementation is available at https://github.com/med-segment/egmfa-net.