2 citations · 3 across the 14 of their papers we have counts for
17 papers
VeCAS: Vessel-Focused Contrast-Free Angiogram Synthesis for Vascular Interventions
De-Xing Huang, Chen-Yu Wang, Hao Liang +10
X-ray angiography relies on iodinated contrast agents to visualize vascular structures during image-guided interventions. However, contrast administration carries risks of adverse…
Vascular anatomy-aware self-supervised pre-training for X-ray angiogram analysis
De-Xing Huang, Chaohui Yu, Xiao-Hu Zhou +8
Objective: X-ray angiography is the gold standard imaging modality for cardiovascular diseases. However, current deep learning approaches for X-ray angiogram analysis are severely…
REASON: Probability map-guided dual-branch fusion framework for gastric content assessment
Nu-Fnag Xiao, De-Xing Huang, Le-Tian Wang +8
Accurate assessment of gastric content from ultrasound is critical for stratifying aspiration risk at induction of general anesthesia. However, traditional methods rely on manual t…
VLA Model Post-Training via Action-Chunked PPO and Self Behavior Cloning
Si-Cheng Wang, Tian-Yu Xiang, Xiao-Hu Zhou +6
Reinforcement learning (RL) is a promising avenue for post-training vision-language-action (VLA) models, but practical deployment is hindered by sparse rewards and unstable trainin…
Online Adaptation via Dual-Stage Alignment and Self-Supervision for Fast-Calibration Brain-Computer Interfaces
Sheng-Bin Duan, Jian-Long Hao, Tian-Yu Xiang +5
Individual differences in brain activity hinder the online application of electroencephalogram (EEG)-based brain computer interface (BCI) systems. To overcome this limitation, this…
Task-Oriented Learning for Automatic EEG Denoising
Tian-Yu Xiang, Zheng Lei, Xiao-Hu Zhou +7
Electroencephalography (EEG) denoising methods typically depend on manual intervention or clean reference signals. This work introduces a task-oriented learning framework for autom…
VasoMIM: Vascular Anatomy-Aware Masked Image Modeling for Vessel Segmentation
De-Xing Huang, Xiao-Hu Zhou, Mei-Jiang Gui +7
Accurate vessel segmentation in X-ray angiograms is crucial for numerous clinical applications. However, the scarcity of annotated data presents a significant challenge, which has…
Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends
Tian-Yu Xiang, Ao-Qun Jin, Xiao-Hu Zhou +11
Vision-language-action (VLA) models extend vision-language models (VLM) by integrating action generation modules for robotic manipulation. Leveraging the strengths of VLM in vision…
CAS-IQA: Teaching Vision-Language Models for Synthetic Angiography Quality Assessment
Bo Wang, De-Xing Huang, Xiao-Hu Zhou +5
Synthetic X-ray angiographies generated by modern generative models hold great potential to reduce the use of contrast agents in vascular interventional procedures. However, low-qu…
VLA Model-Expert Collaboration for Bi-directional Manipulation Learning
Tian-Yu Xiang, Ao-Qun Jin, Xiao-Hu Zhou +8
The emergence of vision-language-action (VLA) models has given rise to foundation models for robot manipulation. Although these models have achieved significant improvements, their…
Learning Novel Skills from Language-Generated Demonstrations
Ao-Qun Jin, Tian-Yu Xiang, Xiao-Hu Zhou +8
Robots are increasingly deployed across diverse domains to tackle tasks requiring novel skills. However, current robot learning algorithms for acquiring novel skills often rely on…
CAS-GAN for Contrast-free Angiography Synthesis
De-Xing Huang, Xiao-Hu Zhou, Mei-Jiang Gui +6
Iodinated contrast agents are widely utilized in numerous interventional procedures, yet posing substantial health risks to patients. This paper presents CAS-GAN, a novel GAN frame…
SPIRONet: Spatial-Frequency Learning and Graph-based Channel Interaction Network for Vessel Segmentation
De-Xing Huang, Xiao-Hu Zhou, Xiao-Liang Xie +8
Automatic vessel segmentation plays a pivotal role in the development of next-generation interventional navigation systems for surgical robotics. However, current approaches still…
MOSformer: Momentum encoder-based inter-slice fusion transformer for medical image segmentation
De-Xing Huang, Xiao-Hu Zhou, Mei-Jiang Gui +6
Medical image segmentation takes an important position in various clinical applications. 2.5D-based segmentation models bridge the computational efficiency of 2D-based models with…
MICRO: Model-Based Offline Reinforcement Learning with a Conservative Bellman Operator
Xiao-Yin Liu, Xiao-Hu Zhou, Guotao Li +5
Offline reinforcement learning (RL) faces a significant challenge of distribution shift. Model-free offline RL penalizes the Q value for out-of-distribution (OOD) data or constrain…
CROP: Conservative Reward for Model-based Offline Policy Optimization
Hao Li, Xiao-Hu Zhou, Shu-Hai Li +6
Offline reinforcement learning (RL) aims to optimize a policy using collected data without online interactions. Model-based approaches are particularly appealing for addressing off…
DOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learning
Xiao-Yin Liu, Xiao-Hu Zhou, Mei-Jiang Gui +7
Model-based reinforcement learning (RL), which learns an environment model from the offline dataset and generates more out-of-distribution model data, has become an effective appro…