activity
20162021
most citedDeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification

64 citations · 184 across the 21 of their papers we have counts for

collaborators

28 papers

cs.CV20214 cited

Test-Time Training for Deformable Multi-Scale Image Registration

Wentao Zhu, Yufang Huang, Daguang Xu +3

Registration is a fundamental task in medical robotics and is often a crucial step for many downstream tasks such as motion analysis, intra-operative tracking and image segmentatio…

eess.IV20203 cited

Spatial Context-Aware Self-Attention Model For Multi-Organ Segmentation

Hao Tang, Xingwei Liu, Kun Han +6

Multi-organ segmentation is one of most successful applications of deep learning in medical image analysis. Deep convolutional neural nets (CNNs) have shown great promise in achiev…

cs.CV20202 cited

MVHM: A Large-Scale Multi-View Hand Mesh Benchmark for Accurate 3D Hand Pose Estimation

Liangjian Chen, Shih-Yao Lin, Yusheng Xie +2

Estimating 3D hand poses from a single RGB image is challenging because depth ambiguity leads the problem ill-posed. Training hand pose estimators with 3D hand mesh annotations and…

cs.CV20203 cited

Temporal-Aware Self-Supervised Learning for 3D Hand Pose and Mesh Estimation in Videos

Liangjian Chen, Shih-Yao Lin, Yusheng Xie +2

Estimating 3D hand pose directly from RGB imagesis challenging but has gained steady progress recently bytraining deep models with annotated 3D poses. Howeverannotating 3D poses is…

cs.CV2020

DGGAN: Depth-image Guided Generative Adversarial Networks for Disentangling RGB and Depth Images in 3D Hand Pose Estimation

Liangjian Chen, Shih-Yao Lin, Yusheng Xie +3

Estimating3D hand poses from RGB images is essentialto a wide range of potential applications, but is challengingowing to substantial ambiguity in the inference of depth in-formati…

cs.CV202011 cited

SIA-GCN: A Spatial Information Aware Graph Neural Network with 2D Convolutions for Hand Pose Estimation

Deying Kong, Haoyu Ma, Xiaohui Xie

Graph Neural Networks (GNNs) generalize neural networks from applications on regular structures to applications on arbitrary graphs, and have shown success in many application doma…