Papers
42
Total Citations
679
H-Index
13
About
Xiao-Liang Xie is a leading researcher at the intersection of computer vision and robotic-assisted surgery, with a primary focus on the real-time semantic segmentation of surgical instruments. His most impactful contributions include the development of attention-guided deep learning architectures, such as RAUNet (126 citations) and RASNet (66 citations), which have significantly advanced the accuracy and efficiency of instrument tracking in cataract and robotic surgery. Xie’s work addresses critical challenges like specular reflection and high computational costs, enabling real-time performance in clinical settings. Beyond segmentation, he has made notable strides in interventional cardiology, designing bio-inspired robotic hands for percutaneous coronary intervention (49 citations) and multifunctional frameworks for guidewire analysis in X-ray fluoroscopy (38 citations). His research also extends to surgical skill assessment and tactile sensing, with papers on dynamic warping manipulations and Halbach-cylinder-based magnetic skins. With over 450 total citations across his top ten papers, Xie’s innovations are shaping the future of minimally invasive surgery, reducing radiation exposure, and enhancing procedural precision. His work is essential reading for anyone interested in deep learning for medical robotics and real-time surgical assistance.
Research Focus
Key Achievements
Top Papers
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- 9Surgical Skill Assessment Based on Dynamic Warping Manipulations21 citations · 2022
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