Papers
5
Total Citations
31
H-Index
3
About
Laiwang Zheng is a researcher at the forefront of AI-driven robotic surgery, specializing in computer vision and medical robotics. His primary research focuses on developing deep learning models for the real-time detection and segmentation of surgical instruments, particularly in the challenging environment of intracranial procedures. Zheng’s major contributions include the creation of SINet, a hybrid deep CNN model that achieved 17 citations for its robust performance in real-time instrument detection and segmentation. He also developed InstrumentNet and MFF-Net, the latter a multiscale feature fusion network designed to overcome issues of occlusion and variable illumination in craniotomy settings. Beyond vision, Zheng has explored mechanical design with a wire-driven continuum minimally invasive surgical robot, addressing dexterity constraints in confined surgical spaces. His work on YOLOv7-based object detection further demonstrates his commitment to real-time surgical assistance. With a growing citation impact, Zheng’s research is pivotal in enhancing surgical safety and autonomy, bridging the gap between advanced AI perception and practical robotic surgery applications.
Research Focus
Key Achievements
Top Papers
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- 4Real Time Surgical Instrument Object Detection Using YOLOv72 citations · 2023
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