Oct 27, 2021 · BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation. Authors:Yanda Meng, Hongrun Zhang, Dongxu ...
proposed a CNN and Graph Convolution. Network (GCN) aggregated network [21] to directly regress the vertices' coordinates of the. OC and OD boundaries. It is ...
smallmax00/BI-GConv: BMVC 2021 Oral: code for BI-GCN: Boundary ...
github.com › smallmax00 › BI-GConv
BMVC 2021 Oral: code for BI-GCN: Boundary-Aware Input-Dependent Graph Convolution for Biomedical Image Segmentation - smallmax00/BI-GConv.
Segmentation is an essential operation of image processing. The convolution operation suffers from a limited receptive field, while global modelling is ...
In this paper, we apply graph convolution into the segmentation task and propose an improved Laplacian. Different from existing methods, our Laplacian is data- ...
This paper applies graph convolution into the segmentation task and proposes an improved \textit{Laplacian} that is well-suited to obtain global semantic ...
Oct 31, 2021 · proposed a CNN and Graph Convolution. Network (GCN) aggregated network [22] to directly regress the vertices' coordinates of the. OC and OD ...
Oct 27, 2021 · This paper proposes a straightforward, intuitive deep learning approach for (biomedical) image segmentation tasks. Different from the existing ...
Nov 22, 2021 · BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation ... Hyperbolic graph embedding with enhanced ...
BI-GCN: boundary-aware input-dependent graph convolution network for biomedical image segmentation. Y Meng, H Zhang, D Gao, Y Zhao, X Yang, X Qian, X Huang ...