MCGNet: Multi-level consistency guided polyp segmentation

Polyp segmentation is challenging due to the varying shapes and sizes, and low contrast, resulting in blurred segmentation boundaries. To address this problem, we propose a multi-level consistency guided network (MCGNet), which performs joint supervision at three different levels: (i) at size level, we obtain two inputs with consistent content and different sizes by applying affine transformation to the input image; (ii) at boundary level, we utilize two modules, Multi-scale Attention (MA) and Feature Similarity Aggregation (FSA), to reinforce the boundary information and learn the boundary consistency in the output layer; (iii) at class activation map level, we follow a consistency regularization approach to restrict the range of class activation maps in the middle layer by Pixel Correlation Attention (PCA) module. Experimental results on 5 widely used datasets show that the MCGNet achieves state-of-the-art performance and exhibits outstanding generality.