Interpolation graph convolutional network for 3D point cloud analysis
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.23087
Abstract: The feature analysis of point clouds, a popular representation of three‐dimensional (3D) objects, is rising as a hot research topic nowadays. Point cloud data bear a sparse and unordered nature, making many commonly used feature… read more here.
Keywords: point cloud; interpolation graph; graph convolutional; point ... See more keywords
Graph-convolutional-network-based interactive prostate segmentation in MR images.
Sign Up to like & getrecommendations! Published in 2020 at "Medical physics"
DOI: 10.1002/mp.14327
Abstract: PURPOSE Accurate and robust segmentation of the prostate from magnetic resonance (MR) images is extensively applied in many clinical applications in prostate cancer diagnosis and treatment. The purpose of this study is the development of… read more here.
Keywords: prostate; graph convolutional; segmentation; method ... See more keywords
Bot prediction on social networks of Twitter in altmetrics using deep graph convolutional networks
Sign Up to like & getrecommendations! Published in 2020 at "Soft Computing"
DOI: 10.1007/s00500-020-04689-y
Abstract: In the context of smart cities, it is crucial to filter out falsified information spread on social media channels through paid campaigns or bot-user accounts that significantly influence communication networks across the social communities and… read more here.
Keywords: social network; graph convolutional; convolutional networks; network ... See more keywords
Joint extraction of entities and overlapping relations by improved graph convolutional networks
Sign Up to like & getrecommendations! Published in 2021 at "Applied Intelligence"
DOI: 10.1007/s10489-021-02667-x
Abstract: Joint extraction of entities and relations is to recognize entities and semantic relations simultaneously, which is significant for knowledge graph construction. Recently, many effective joint models use dependency trees to capture the structural information of… read more here.
Keywords: information; convolutional networks; graph convolutional; joint extraction ... See more keywords
MutagenPred-GCNNs: A Graph Convolutional Neural Network-Based Classification Model for Mutagenicity Prediction with Data-Driven Molecular Fingerprints.
Sign Up to like & getrecommendations! Published in 2021 at "Interdisciplinary sciences, computational life sciences"
DOI: 10.1007/s12539-020-00407-2
Abstract: An important task in the early stage of drug discovery is the identification of mutagenic compounds. Mutagenicity prediction models that can interpret relationships between toxicological endpoints and compound structures are especially favorable. In this research,… read more here.
Keywords: graph convolutional; convolutional neural; mutagenicity prediction; model ... See more keywords
Graph convolutional network-based semi-supervised feature classification of volumes
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Visualization"
DOI: 10.1007/s12650-021-00787-7
Abstract: Abstract Feature classification has always been one of the research hotspots in scientific visualization. However, conventional interactive feature classification methods rely on prior knowledge and typically require trial and error, whereas feature classification based on… read more here.
Keywords: classification; semi supervised; feature classification; network ... See more keywords
Exploring noncoding RNAs in thyroid cancer using a graph convolutional network approach.
Sign Up to like & getrecommendations! Published in 2022 at "Computers in biology and medicine"
DOI: 10.1016/j.compbiomed.2022.105447
Abstract: Noncoding RNAs (ncRNAs) are crucial regulators in initiating and promoting thyroid cancer. Exploring the relationship between ncRNAs and thyroid cancer is essential for the diagnosis and treatment of thyroid cancer. Wet-lab experiments are costly and… read more here.
Keywords: graph convolutional; cancer; noncoding rnas; thyroid cancer ... See more keywords
Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites
Sign Up to like & getrecommendations! Published in 2022 at "EBioMedicine"
DOI: 10.1016/j.ebiom.2022.103977
Abstract: Summary Background Establishing objective and quantitative neuroimaging biomarkers at individual level can assist in early and accurate diagnosis of major depressive disorder (MDD). However, most previous studies using machine learning to identify MDD were based… read more here.
Keywords: depressive disorder; major depressive; graph convolutional; mdd ... See more keywords
A graph convolutional neural network for classification of building patterns using spatial vector data
Sign Up to like & getrecommendations! Published in 2019 at "ISPRS Journal of Photogrammetry and Remote Sensing"
DOI: 10.1016/j.isprsjprs.2019.02.010
Abstract: Abstract Machine learning methods, specifically, convolutional neural networks (CNNs), have emerged as an integral part of scientific research in many disciplines. However, these powerful methods often fail to perform pattern analysis and knowledge mining with… read more here.
Keywords: neural network; spatial vector; graph convolutional; vector data ... See more keywords
Building segmentation through a gated graph convolutional neural network with deep structured feature embedding
Sign Up to like & getrecommendations! Published in 2020 at "Isprs Journal of Photogrammetry and Remote Sensing"
DOI: 10.1016/j.isprsjprs.2019.11.004
Abstract: Automatic building extraction from optical imagery remains a challenge due to, for example, the complexity of building shapes. Semantic segmentation is an efficient approach for this task. The latest development in deep convolutional neural networks… read more here.
Keywords: network; graph convolutional; segmentation; convolutional neural ... See more keywords
Disease prediction with edge-variational graph convolutional networks
Sign Up to like & getrecommendations! Published in 2022 at "Medical image analysis"
DOI: 10.1016/j.media.2022.102375
Abstract: The need for computational models that can incorporate imaging data with non-imaging data while investigating inter-subject associations arises in the task of population-based disease analysis. Although off-the-shelf deep convolutional neural networks have empowered representation learning… read more here.
Keywords: graph convolutional; population; disease; disease prediction ... See more keywords