12 citations · 21 across the 9 of their papers we have counts for
7 papers · 1 filter
DBT-Bleed: Dual-Branch Temporal Modeling with Key-Frame Selection for Surgical Bleeding Detection
Sudhanshu Mishra, Jialang Xu, Jensen Ang +3
Intraoperative Adverse Events (IAEs) detection is critical for improving surgical safety, with bleeding being among the most frequent events across many surgery types. Existing met…
CoRe-DA: Contrastive Regression for Unsupervised Domain Adaptation in Surgical Skill Assessment
Dimitrios Anastasiou, Razvan Caramalau, Jialang Xu +6
Vision-based surgical skill assessment (SSA) enables objective and scalable evaluation of operative performance. Progress in this field is constrained by the high cost and time dem…
Temporal Cluster Assignment for Efficient Real-Time Video Segmentation
Ka-Wai Yung, Felix J. S. Bragman, Jialang Xu +3
Vision Transformers have substantially advanced the capabilities of segmentation models across both image and video domains. Among them, the Swin Transformer stands out for its abi…
StereoMamba: Real-time and Robust Intraoperative Stereo Disparity Estimation via Long-range Spatial Dependencies
Xu Wang, Jialang Xu, Shuai Zhang +3
Stereo disparity estimation is crucial for obtaining depth information in robot-assisted minimally invasive surgery (RAMIS). While current deep learning methods have made significa…
Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery
Jialang Xu, Jiacheng Wang, Lequan Yu +3
Personalized federated learning (PFL) for surgical instrument segmentation (SIS) is a promising approach. It enables multiple clinical sites to collaboratively train a series of mo…
Think Step by Step: Chain-of-Gesture Prompting for Error Detection in Robotic Surgical Videos
Zhimin Shao, Jialang Xu, Danail Stoyanov +2
Despite significant advancements in robotic systems and surgical data science, ensuring safe and optimal execution in robot-assisted minimally invasive surgery (RMIS) remains a com…
SEDMamba: Enhancing Selective State Space Modelling with Bottleneck Mechanism and Fine-to-Coarse Temporal Fusion for Efficient Error Detection in Robot-Assisted Surgery
Jialang Xu, Nazir Sirajudeen, Matthew Boal +3
Automated detection of surgical errors can improve robotic-assisted surgery. Despite promising progress, existing methods still face challenges in capturing rich temporal context t…