Hierarchical Refinement and Bilateral Attention Fusion for Polyp Segmentation

In the field of medical imaging, polyp segmentation is a crucial task as it enables doctors to accurately identify and segment polyps in endoscopic images and other medical images. Currently, numerous deep learning based polyp segmentation models mainly rely on multi-scale feature fusion techniques to delineate the boundaries of polyps. However, these existing methods often fail to consider the interconnection between the model's localization and segmentation processes. Generally, when searching for polyps, people first determine the approximate location of the polyps and then gradually obtain detailed feature information of the polyps. In view of this, we propose a hierarchical refinement multi-scale feature fusion Model named HRFFNet. First, we design a hierarchical refinement feature extraction method to precisely optimize the initially located polyp regions. Then, we develop a feature fusion block named FB, which relies on the overall lesion information to form multi-scale feature representations. Through extensive experiments on four commonly used benchmark datasets, we find that HRFFNet performs outstandingly in polyp segmentation, and its performance significantly surpasses that of existing top-notch models.