Papers
4
Total Citations
75
H-Index
4
About
Wen-Qian Yue is a leading researcher in computer-assisted surgery, with a focus on surgical instrument segmentation, motion estimation, and intra-operative scene understanding. Their work addresses critical challenges in robot-assisted surgery, particularly the accurate detection and tracking of instruments and soft tissue under occlusion. Yue’s most cited paper, “SurgiNet: Pyramid Attention Aggregation and Class-wise Self-Distillation for Surgical Instrument Segmentation” (2021, 55 citations), introduces a novel deep learning architecture that significantly improves segmentation accuracy in complex surgical scenes. This work has become a foundational reference in the field. More recently, Yue proposed the Motion Decoupling Network (2023, 7 citations), which tackles intra-operative motion estimation under occlusion—a persistent problem in real-time surgical guidance. Other notable contributions include fast tissue segmentation for micro-neurosurgery using high correlative non-local networks (2023, 7 citations) and variation-learning high-resolution networks for capsulorhexis recognition in cataract surgery (2023, 6 citations). Yue’s research bridges computer vision and clinical practice, enabling safer, more precise robotic interventions. With a growing citation impact, Yue is shaping the future of intelligent surgical systems.
Research Focus
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