Cross-level Feature Aggregation Network for Polyp Segmentation

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Cross-level Feature Aggregation Network for Polyp Segmentation
Cross-level Feature Aggregation Network for Polyp Segmentation
Journal Title:
Pattern Recognition
Publication Date:
26 March 2023
Zhou, T., Zhou, Y., He, K., Gong, C., Yang, J., Fu, H., & Shen, D. (2023). Cross-level Feature Aggregation Network for Polyp Segmentation. Pattern Recognition, 140, 109555.
Accurate segmentation of polyps from colonoscopy images plays a critical role in the diagnosis and cure of colorectal cancer. Although effectiveness has been achieved in the field of polyp segmentation, there are still several challenges. Polyps often have a diversity of size and shape and have no sharp boundary between polyps and their surrounding. To address these challenges, we propose a novel Cross-level Feature Aggregation Network (CFA-Net) for polyp segmentation. Specifically, we first propose a boundary prediction network to generate boundary-aware features, which are incorporated into the segmentation network using a layer-wise strategy. In particular, we design a two-stream structure based segmentation network, to exploit hierarchical semantic information from cross-level features. Furthermore, a Cross-level Feature Fusion (CFF) module is proposed to integrate the adjacent features from different levels, which can characterize the cross-level and multi-scale information to handle scale variations of polyps. Further, a Boundary Aggregated Module (BAM) is proposed to incorporate boundary information into the segmentation network, which enhances these hierarchical features to generate finer segmentation maps. Quantitative and qualitative experiments on five public datasets demonstrate the effectiveness of our CFA-Net against other state-of-the-art polyp segmentation methods.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the A*STAR - Central Research Fund
Grant Reference no. : NA

This work was supported in part by the National Natural Science Foundation of China (Nos. 62172228, 62106043, 61973162), NSF of Jiangsu Province (No: BZ2021013), CAAI-Huawei Mind-Spore Open Fund, NSF for Distinguished Young Scholar of Jiangsu Province (No: BK20220080), the Fundamental Research Funds for the Central Universities (Nos: 30920032202, 30921013114), and an Open Project of the Key Laboratory of System Control and Information Processing, Ministry of Education (Shanghai Jiao Tong University, ID: Scip202102)
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