SAEFormer: stepwise attention emphasis transformer for polyp segmentation.

Polyp segmentation in colorectal images is the most effective and necessary tool for the early detection of colorectal cancer, and deep learning has become popular for efficiently segmenting polyps. The complex morphological characteristics of polyps, such as the unclear boundary between polyps and mucosa, and the lack of training data could cause great difficulties in network fitting. Transformer-based semantic segmentation networks have achieved more promising performance than traditional convolutional neural networks. However, the dispersion of self-attention and the less accurate local feature recognition limit the further development and applications of Transformer-based networks. This paper proposes a novel Stepwise Attention Emphasis module to refocus self-attention for Transformer-based polyp segmentation in colorectal images, where a reverse fuse module is used to better fuse different levels of features. Furthermore, a new decoder network, called the densely smooth fusion decoder, is also proposed to enhance local details and provide more useful information from deep features to shallow features. Experimental comparisons are conducted, and result analysis shows that the proposed network achieves promising performance in both learning and generalization ability on public datasets. [ABSTRACT FROM AUTHOR]