期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Make U-Net Greater: An Easy-to-Embed Approach to Improve Segmentation Performance Using Hypergraph
1
作者 Jing Peng jingfu yang +5 位作者 Chaoyang Xia Xiaojie Li Yanfen Guo Ying Fu Xinlai Chen Zhe Cui 《Computer Systems Science & Engineering》 SCIE EI 2022年第7期319-333,共15页
semantics information while maintaining spatial detail con-texts.Long-range context information plays a crucial role in this scenario.How-ever,the traditional convolution kernel only provides the local and small size ... semantics information while maintaining spatial detail con-texts.Long-range context information plays a crucial role in this scenario.How-ever,the traditional convolution kernel only provides the local and small size of the receptivefield.To address the problem,we propose a plug-and-play module aggregating both local and global information(aka LGIA module)to capture the high-order relationship between nodes that are far apart.We incorporate both local and global correlations into hypergraph which is able to capture high-order rela-tionships between nodes via the concept of a hyperedge connecting a subset of nodes.The local correlation considers neighborhood nodes that are spatially adja-cent and similar in the same CNN feature maps of magnetic resonance(MR)image;and the global correlation is searched from a batch of CNN feature maps of MR images in feature space.The influence of these two correlations on seman-tic segmentation is complementary.We validated our LGIA module on various CNN segmentation models with the cardiac MR images dataset.Experimental results demonstrate that our approach outperformed several baseline models. 展开更多
关键词 Convolutional neural network semantic segmentation hypergraph neural network LGIA module
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部