.png){kind=icon}
返回
ColonFormer: An Efficient Transformer Based Method for Colon Polyp Segmentation
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2022.3195241.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Identifying polyps is challenging for automatic analysis of endoscopic images in computer-aided clinical support systems. Models based on convolutional networks (CNN), transformers, and their combinations have been proposed to segment polyps with promising results. However, those approaches have limitations either in modeling the local appearance of the polyps only or lack of multi-level feature representation for spatial dependency in the decoding process. This paper proposes a novel network, namely ColonFormer, to address these limitations. ColonFormer is an encoder-decoder architecture capable of modeling long-range semantic information at both encoder and decoder branches. The encoder is a lightweight architecture based on transformers for modeling global semantic relations at multi scales. The decoder is a hierarchical network structure designed for learning multi-level features to enrich feature representation. Besides, a refinement module is added with a new skip connection technique to refine the boundary of polyp objects in the global map for accurate segmentation. Extensive experiments have been conducted on five popular benchmark datasets for polyp segmentation, including Kvasir, CVC-Clinic DB, CVC-ColonDB, CVC-T, and ETIS-Larib. Experimental results show that our ColonFormer outperforms other state-of-the-art methods on all benchmark datasets. Our code is available at: https://github.com/ducnt9907/ColonFormer.
Keyword:
Transformers
Image segmentation
Computer architecture
Decoding
Feature extraction
Convolutional neural networks
Computational modeling
Polyp segmentation
deep learning
encoder-decoder network
hierarchical multi-scale CNN
computer-aided diagnosis
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Inflexibility of mental planning: A characteristic disorder with prefrontal lobe lesions?心理计划的僵化: 前额叶病变的特征性障碍？
PREAI
分享收藏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Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis.png)
GASTROENTEROLOGY
GASTROENTEROLOGYIF25.1
PREAI
分享收藏MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentationMultiusunnet: 重新思考多模态生物医学图像分割的u-net体系结构NEURAL NETWORKS
OAAI
NEURAL NETWORKSIF6.3
OAAI分享收藏Computer-Aided Classification of Gastrointestinal Lesions in Regular Colonoscopy常规结肠镜检查中胃肠道病变的计算机辅助分类OAAI
OAAI分享收藏Artificial intelligence and colonoscopy: Current status and future perspectivesDIGESTIVE ENDOSCOPYOAAI
DIGESTIVE ENDOSCOPYIF4.7
OAAI分享收藏Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning基于深度学习的结肠镜实时息肉检测、定位和分割IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容