  Given the close association between colorectal cancer and polyps, the
diagnosis and identification of colorectal polyps play a critical role in the
detection and surgical intervention of colorectal cancer. In this context, the
automatic detection and segmentation of polyps from various colonoscopy images
has emerged as a significant problem that has attracted broad attention.
Current polyp segmentation techniques face several challenges: firstly, polyps
vary in size, texture, color, and pattern; secondly, the boundaries between
polyps and mucosa are usually blurred, existing studies have focused on
learning the local features of polyps while ignoring the long-range
dependencies of the features, and also ignoring the local context and global
contextual information of the combined features. To address these challenges,
we propose FLDNet (Foreground-Long-Distance Network), a Transformer-based
neural network that captures long-distance dependencies for accurate polyp
segmentation. Specifically, the proposed model consists of three main modules:
a pyramid-based Transformer encoder, a local context module, and a
foreground-Aware module. Multilevel features with long-distance dependency
information are first captured by the pyramid-based transformer encoder. On the
high-level features, the local context module obtains the local characteristics
related to the polyps by constructing different local context information. The
coarse map obtained by decoding the reconstructed highest-level features guides
the feature fusion process in the foreground-Aware module of the high-level
features to achieve foreground enhancement of the polyps. Our proposed method,
FLDNet, was evaluated using seven metrics on common datasets and demonstrated
superiority over state-of-the-art methods on widely-used evaluation measures.
