2 citations · 5 across the 18 of their papers we have counts for
22 papers
CLUE: Clinically Aligned Patient-Level Ultrasound Diagnosis via Evidence-Aware Multi-Instance Learning
Qin-Yi Zhang, Linying Cao, De-Xing Huang +7
Patient-level ultrasound diagnosis demands not only high predictive accuracy but also clinically aligned mitigation of asymmetric diagnostic risks. While Multi-Instance Learning (M…
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…
DINO-Med3D: Bridging Dimension and Domain Gaps in Volumetric Segmentation via Progressive Adaptation
Haoyu Hu, Xiyao Ma, Shiqi Liu +4
Although DINOv3 has demonstrated remarkable semantic discrimination in natural imagery, its direct application to volumetric medical segmentation is hindered by inherent dimension…
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…
LEASE: Offline Preference-based Reinforcement Learning with High Sample Efficiency
Xiao-Yin Liu, Guotao Li, Xiao-Hu Zhou +1
Offline preference-based reinforcement learning (PbRL) provides an effective way to overcome the challenges of designing reward and the high costs of online interaction. However, s…
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…
Robotic transcatheter tricuspid valve replacement with hybrid enhanced intelligence: a new paradigm and first-in-vivo study
Shuangyi Wang, Haichuan Lin, Yiping Xie +10
Transcatheter tricuspid valve replacement (TTVR) is the latest treatment for tricuspid regurgitation and is in the early stages of clinical adoption. Intelligent robotic approaches…
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…
A Weight-aware-based Multi-source Unsupervised Domain Adaptation Method for Human Motion Intention Recognition
Xiao-Yin Liu, Guotao Li, Xiao-Hu Zhou +2
Accurate recognition of human motion intention (HMI) is beneficial for exoskeleton robots to improve the wearing comfort level and achieve natural human-robot interaction. A classi…
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…