FLDNet: A Foreground-Aware Network for Polyp Segmentation Leveraging Long-Distance Dependencies

• URL: http://arxiv.org/abs/2309.05987v1
• Date: Tue, 12 Sep 2023 06:32:42 GMT
• Title: FLDNet: A Foreground-Aware Network for Polyp Segmentation Leveraging Long-Distance Dependencies
• Authors: Xuefeng Wei, Xuan Zhou
• Abstract summary: We propose FLDNet, a Transformer-based neural network that captures long-distance dependencies for accurate polyp segmentation. 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.
• Score: 1.7623838912231695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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