Regularized graph structure learning with semantic knowledge for multi-variates time-series forecasting
Multivariate time-series forecasting is a critical task for many applications, and graph time-
series network is widely studied due to its capability to capture the spatial-temporal …
series network is widely studied due to its capability to capture the spatial-temporal …
[HTML][HTML] Deep learning on spatiotemporal graphs: a systematic review, methodological landscape, and research opportunities
A Zeghina, A Leborgne, F Le Ber, A Vacavant - Neurocomputing, 2024 - Elsevier
Deep learning approaches, given their low cost and high reliability, have gained much
popularity in different subjects, such as computer vision and natural language processing …
popularity in different subjects, such as computer vision and natural language processing …
Cardiac magnetic resonance radiomics for disease classification
X Zhang, C Cui, S Zhao, L Xie, Y Tian - European Radiology, 2023 - Springer
Objectives This study investigated the discriminability of quantitative radiomics features
extracted from cardiac magnetic resonance (CMR) images for hypertrophic cardiomyopathy …
extracted from cardiac magnetic resonance (CMR) images for hypertrophic cardiomyopathy …
Hit-gcn: spatial-temporal graph convolutional network embedded with heterogeneous information of road network for traffic forecasting
H Xiong, G Shen, X Lan, H Yuan, X Kong - Electronics, 2023 - mdpi.com
In road networks, attribute information carried by road segment nodes, such as weather and
points of interest (POI), exhibit strong heterogeneity and often involve one-to-many or many …
points of interest (POI), exhibit strong heterogeneity and often involve one-to-many or many …
Multiscale graph convolutional networks for cardiac motion analysis
We propose a multiscale spatio-temporal graph convolutional network (MST-GCN)
approach to learn the left ventricular (LV) motion patterns from cardiac MR image …
approach to learn the left ventricular (LV) motion patterns from cardiac MR image …
Real-time human falling recognition via spatial and temporal self-attention augmented graph convolutional network
Currently, the skeleton-based human action recognition (eg walking, sitting and falling
down) has achieved great interest, because the skeleton graph is robust to complex …
down) has achieved great interest, because the skeleton graph is robust to complex …
End-To-End Deformable Attention Graph Neural Network for Single-View Liver Mesh Reconstruction
Intensity modulated radiotherapy (IMRT) is one of the most common modalities for treating
cancer patients. One of the biggest challenges is precise treatment delivery that accounts for …
cancer patients. One of the biggest challenges is precise treatment delivery that accounts for …
MANET: Mitral Annulus Point Tracking Network in Cardiac Magnetic Resonance
Cardiac magnetic resonance (CMR) imaging is frequently recommended for patients at
intermediate risk of cardiovascular disease to triage them for medication or invasive …
intermediate risk of cardiovascular disease to triage them for medication or invasive …
A Motion-Aware DNN Model with Edge Focus Loss and Quality Control for Short-Axis Left Ventricle Segmentation of Cine MR Sequences
Y Wang, Z Sun, Z Liu, J Lu, N Zhang - Journal of Imaging Informatics in …, 2024 - Springer
Accurate segmentation of the left ventricle myocardium is the key step of automatic
assessment of cardiac function. However, the current methods mainly focus on the end …
assessment of cardiac function. However, the current methods mainly focus on the end …