In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its
Following this trend, various deep learning architectures using convolutional neural networks (CNNs) have been used in the field of computer vision. Poma et al. applied the fuzzy gravitational search algorithm to CNNs to optimize parameters and verified its performance via image recognition experiments...
In this paper, a novel DL-based fault diagnosis method, based on 2D map representations of Cyclic Spectral Coherence (CSCoh) and Convolutional Neural Networks (CNN), is proposed to improve the recognition performance of rolling element bearing faults. Firstly, the 2D CSCoh maps of vibration ...
Spectral-Spatial Graph Convolutional Networks for Semel-Supervised Hyperspectral Image Classification. IEEE Trans. Geosci. Remote Sens. Lett. 2019, 16, 241–245. [Google Scholar] [CrossRef] Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Kaiser, L.; ...
In recent years, deep learning algorithms, including Recurrent Neural Networks (RNN) [24], Convolutional Neural Networks (CNN) [25], Graph Neural Networks (GNN) [26], the newly emerged Transformer [27], and so on, have been innovatively introduced from the computer vision (CV) field to ...
Guo K, Zou D, Chen X (2016) 3D mesh labeling via deep convolutional neural networks. ACM Trans Graph 35(1):3:1–3:12. https://doi.org/10.1145/2835487 Article Google Scholar Hanocka R, Hertz A, Fish N, Giryes R, Fleishman S, Cohen-Or D (2019) MeshCNN: a network with an edge...
Skeleton-based action recognition has been a hot topic with the increasing development of Graph Convolutional Networks(GCNs). Previous work constructed the skeleton sequences into graphs and focused on extracting spatial-temporal information from various actions. However, they ignored the hidden ...
Moreover, by decoding the holistic FCN from various perspectives through multiple spectral Graph Convolutional Neural Networks and fusing their results with decision-level ensembles, we further improve the performance of MDD diagnosis. Our approach is easily implemented with minimal modifications to ...
graph convolutional networksIn recent years, convolution neural networks (CNNs) and graph convolution networks (GCNs) have been widely used in hyperspectral image classification (HSIC). CNNs can effectively extract the spatial spectral features of hyperspectral images (HSIs), while GCNs can quickly ...
[35] used graph embedding and CNN models to extract spectral-spatial features. Nowadays, more and more CNN frameworks that are effective in the field of computer vision are fully utilized in hyperspectral classification tasks, such as ResNet [36], SENet [37], and provide a breakthrough for ...