Yui-Lun Ng

Papers

Total Citations

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About

Yui-Lun Ng is a rising researcher at the intersection of surgical robotics and computer vision, with a focus on enhancing the autonomy and precision of flexible robotic instruments in minimally invasive procedures. His most-cited work, "Shape-Guided Configuration-Aware Learning for Endoscopic-Image-Based Pose Estimation of Flexible Robotic Instruments" (2024), introduces a novel learning framework that integrates shape priors and configuration awareness to accurately estimate the 3D pose of flexible tools from endoscopic images—a critical challenge for real-time surgical navigation and robotic control. This contribution addresses a key bottleneck in robot-assisted surgery, where traditional methods struggle with occlusions and non-rigid deformations. Though early in his career, Ng’s work has already garnered attention, with his top paper accumulating 3 citations in its first year, signaling growing impact in the surgical robotics community. His research promises to improve the safety and efficacy of flexible endoscopy and robotic interventions, laying groundwork for more intelligent, context-aware surgical systems. Ng’s innovative approach to combining geometric reasoning with deep learning marks him as a promising voice in advancing computer-assisted surgery.