Results 101 to 110 of about 3,278,052 (298)

Multiscale Spatial Fusion Feature‐Driven Characterization of Gastric Cancer Invasive Margins: A Multicenter Cohort Study for Preoperative Accurate Differentiation Between T4a and T4b Subtypes

Advanced Science, EarlyView.
This study develops a multi‐dimensional vision Transformer‐based model, GAVR, to accurately distinguish gastric cancer T4a/b stages preoperatively. Validated across multi‐center cohorts, it achieves excellent performance and significantly improves radiologists’ diagnostic accuracy, offering a promising tool for clinical decision‐making.
Guoliang Zheng, Xiaomiao Chai, Peng Jin, Xin Xin, Yingjie Li, Chun Yang, Jia Wei, Zishuo Yan, Jingyu Zhang, Qianning Zhao, Yingchen Han, Ning Zhang, Fuze Li, Bo Qiao, Huan Wang, Huachuan Zheng, Yan Li, Xin Zhang, Yan Zhao, Wenjun Mao, Jing Zhang   +20 more
wiley   +1 more source

Bayesian graph convolutional network with partial observations.

PLoS ONE
As a widely studied model in the machine learning and data processing society, graph convolutional network reveals its advantage in non-grid data processing.
Shuhui Luo, Peilan Liu, Xulun Ye
doaj   +1 more source

Graph Neural Networks: A Bibliometric Mapping of the Research Landscape and Applications

Information
Graph neural networks (GNNs) are deep learning algorithms that process graph-structured data and are suitable for applications such as social networks, physical models, financial markets, and molecular predictions.
Annielle Mendes Brito da Silva, Natiele Carla da Silva Ferreira, Luiza Amara Maciel Braga, Fabio Batista Mota, Victor Maricato, Luiz Anastacio Alves   +5 more
doaj   +1 more source

DDSurfer: A Weakly‐Supervised Dual‐Stream Deep Learning Framework for Cortical Surface Reconstruction From Diffusion MRI

Advanced Science, EarlyView.
DDSurfer reconstructs cortical surfaces directly from diffusion MRI without requiring T1‐weighted scans. By fusing complementary microstructural features and learning diffeomorphic deformations, it efficiently generates accurate white matter and pial surfaces, improving geometric fidelity and morphometric reliability across datasets for robust surface ...
Chengjin Li, Wei Zhang, Xi Zhu, Yuqian Chen, Nir A. Sochen, Jarrett Rushmore, Carl‐Fredrik Westin, Yogesh Rathi, Lauren J. O'Donnell, Ofer Pasternak, Fan Zhang   +10 more
wiley   +1 more source

SMS spam detection using BERT and multi-graph convolutional networks

International Journal of Intelligent Networks
The surge in smartphone usage has significantly increased Short Message Service (SMS) traffic and, consequently, SMS spam, posing risks such as phishing, financial losses, and privacy breaches.
Linjie Shen, Yanbin Wang, Zhao Li, Wenrui Ma   +3 more
doaj   +1 more source

Enhancing Super‐Resolution Spatial Transcriptomics Data by Transfer Learning

Advanced Science, EarlyView.
SpotZoomer employs a transfer‐learning‐based strategy to enhance the resolution of Visium data by leveraging available high‐resolution priors. The resulting super‐resolved maps enable sharper delineation of cell boundaries and more precise inference of cell–cell communication patterns that would otherwise remain obscured at native resolution.
Xiaoyu Li, Lihua Zhang, Wenwen Min
wiley   +1 more source

Graph Convolutional Recommendation System Based on Bilateral Attention Mechanism

Journal of Engineering
Collaborative Filtering Recommender Systems face data sparsity and cold-start issues, leading to a decrease in their recommendation performance. Therefore, numerous researchers have integrated knowledge graphs and graph convolutional networks into ...
Hui Yang, Changchun Yang
doaj   +1 more source

A meta-learning enhanced dynamic graph convolutional network for cross-region financial risk propagation prediction

Alexandria Engineering Journal
The increasing interconnectedness of global financial systems has amplified the risk of cross-regional financial contagion, posing significant challenges to economic stability.
Chao Zhang, Yingyue Hu
doaj   +1 more source

From Spectral Graph Convolutions to Large Scale Graph Convolutional Networks

CoRR, 2022
Graph Convolutional Networks (GCNs) have been shown to be a powerful concept that has been successfully applied to a large variety of tasks across many domains over the past years. In this work we study the theory that paved the way to the definition of GCN, including related parts of classical graph theory.
openaire   +3 more sources

Cancer‐Associated BCL‐2 Mutants Reveal Mechanisms Towards Venetoclax Resistance

Advanced Science, EarlyView.
Venetoclax (VEN) resistance in chronic lymphocytic leukemia arises from diverse BCL2 mutations. We map mechanisms contributing to VEN resistance across common BCL‐2 variants. G101V and D103Y reduce drug binding and increase sequestration of pro‐apoptotic proteins. V156D blocks VEN allosterically.
Jonas Aufdermauer, Ian de Ridder, Mahjoobeh Ehsani, Marasim Khan, Justin Kale, Lukas P. Frenzel, David Andrews, Spyridoula Karamanou, Ana J. Garcia‐Saez, Geert Bultynck   +9 more
wiley   +1 more source