Papers
37
Total Citations
925
H-Index
18
About
Yueming Jin is a pioneering researcher at the intersection of computer vision, machine learning, and robot-assisted minimally invasive surgery. His work centers on three interconnected domains: surgical instrument segmentation, gesture and workflow recognition, and autonomous surgical robotics — areas where his contributions have fundamentally advanced the field's technical capabilities. Jin's most impactful work addresses the challenge of precisely tracking and segmenting surgical instruments in endoscopic video, earning over 120 citations for his temporal motion flow approach and contributing to landmark community benchmarks like ROBUST-MIS 2019 (89 citations). His research on surgical gesture recognition — leveraging reinforcement learning, tree search, and relational graph learning across video and kinematics data — has helped lay the groundwork for intelligent cognitive assistance systems that could transform surgical training and safety. The HeiChole benchmark study (96 citations) exemplifies his commitment to rigorous comparative validation across the broader research community. More recently, Jin has pushed boundaries in scene reconstruction using Gaussian Splatting and referring-based instrument segmentation, signaling a forward-looking research vision. With over 600 cumulative citations across a decade of work, Jin has established himself as an essential contributor to the next generation of smart, context-aware surgical systems.
Research Focus
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