EEMS: Edge-Prompt Enhanced Medical Image Segmentation Based on Learnable Gating Mechanism
This work addresses segmentation challenges in medical imaging for clinical applications, but it appears incremental as it builds on existing methods with hybrid components.
The paper tackles the problem of medical image segmentation by introducing EEMS, a model that combines edge-aware enhancement and multi-scale prompt generation to improve boundary definition and target localization, achieving superior performance on datasets like ISIC2018.
Medical image segmentation is vital for diagnosis, treatment planning, and disease monitoring but is challenged by complex factors like ambiguous edges and background noise. We introduce EEMS, a new model for segmentation, combining an Edge-Aware Enhancement Unit (EAEU) and a Multi-scale Prompt Generation Unit (MSPGU). EAEU enhances edge perception via multi-frequency feature extraction, accurately defining boundaries. MSPGU integrates high-level semantic and low-level spatial features using a prompt-guided approach, ensuring precise target localization. The Dual-Source Adaptive Gated Fusion Unit (DAGFU) merges edge features from EAEU with semantic features from MSPGU, enhancing segmentation accuracy and robustness. Tests on datasets like ISIC2018 confirm EEMS's superior performance and reliability as a clinical tool.