.png){kind=icon}
返回
Cross-level Feature Aggregation Network for Polyp Segmentation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2023.109555.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 bound-ary 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 segmen-tation network, which enhances these hierarchical features to generate finer segmentation maps. Quan-titative and qualitative experiments on five public datasets demonstrate the effectiveness of our CFA-Net against other state-of-the-art polyp segmentation methods. The source code and segmentation maps will be released at https://github.com/taozh2017/CFANet .(c) 2023 Elsevier Ltd. All rights reserved.
Keyword:
Polyp segmentation
boundary-aware features
cross-level feature fusion
boundary aggregated module
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians在结肠镜检查中准确突出显示息肉的wm-dova地图: 医生的验证与显著性地图
PREAI
分享收藏Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker.png)
PATTERN RECOGNITION
OAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏
分享收藏分享收藏分享收藏
查看更多内容