Accurate　polyp　segmentation　is　crucial　for　early　diagnosis　and　treatment　of　colorectal　cancer.　This　is　a　challenging　task　for　three　main　reasons:　(i)　the　problem　of　model　overfitting　and　weak　generalization　due　to　the　multi-center　distribution　of　data;　(ii)　the　problem　of　interclass　ambiguity　caused　by　motion　blur　and　overexposure　to　endoscopic　light;　and　(iii)　the　problem　of　intraclass　inconsistency　caused　by　the　variety　of　morphologies　and　sizes　of　the　same　type　of　polyps.　To　address　these　challenges,　we　propose　a　new　high-precision　polyp　segmentation　framework,　MEFA-Net,　which　consists　of　three　modules,　including　the　plug-and-play　Mask　Enhancement　Module　(MEG),　Separable　Path　Attention　Enhancement　Module　(SPAE),　and　Dynamic　Global　Attention　Pool　Module　(DGAP).　Specifically,　firstly,　the　MEG　module　regionally　masks　the　high-energy　regions　of　the　environment　and　polyps　through　a　mask,　which　guides　the　model　to　rely　on　only　a　small　amount　of　information　to　distinguish　between　polyps　and　background　features,　avoiding　the　model　from　overfitting　the　environmental　information,　and　improving　the　robustness　of　the　model.　At　the　same　time,　this　module　can　effectively　counteract　the　＇dark　corner　phenomenon＇　in　the　dataset　and　further　improve　the　generalization　performance　of　the　model.　Next,　the　SPAE　module　can　effectively　alleviate　the　inter-class　fuzzy　problem　by　strengthening　the　feature　expression.　Then,　the　DGAP　module　solves　the　intra-class　inconsistency　problem　by　extracting　the　invariance　of　scale,　shape　and　position.　Finally,　we　propose　a　new　evaluation　metric,　MultiColoScore,　for　comprehensively　evaluating　the　segmentation　performance　of　the　model　on　five　datasets　with　different　domains.　We　evaluated　the　new　method　quantitatively　and　qualitatively　on　five　datasets　using　four　metrics.　Experimental　results　show　that　MEFA-Net　significantly　improves　the　accuracy　of　polyp　segmentation　and　outperforms　current　state-of-the-art　algorithms.　Code　posted　on　https://github.com/847001315/MEFA-Net.　©　2024