CCBANet: Cascading Context and Balancing Attention for Polyp Segmentation

Polyps detection plays an important role in colonoscopy, cancer diagnosis, and early treatment. Many efforts have been made to improve the encoder-decoder framework using the global feature with an attention mechanism to enhance local features, helping to effectively segment diversity polyps. However, using only global information derived from the last encoder block leads to the loss of regional information from intermediate layers. Furthermore, defining the boundaries of some polyps is challenging because there is visual interference between the benign region and the polyps at the border. To address these problems, we propose two novel modules: the Cascading Context module (CCM) and the Attention Balance module (BAM), aiming to build an effective polyp segmentation model. Specifically, CCM combines the extracted regional information of the current layer and the lower layer, then pours it into the upper layer - fusing regional and global information analogous to a waterfall pattern. The BAM uses the prediction output of the adjacent lower layer as a guide map to implement the attention mechanism for the three regions separately: the background, polyp, and boundary curve. BAM enhances local context information when deriving features from the encoder block. Our proposed approach is evaluated on three benchmark datasets with six evaluation metrics for segmentation quality and gives competitive results compared to other advanced methods, for both accuracy and efficiency. Code is available at https://github.com/ntcongvn/CCBANet.