Kanqi Wang
Papers
1
Total Citations
1
H-Index
1
About
Kanqi Wang is a rising researcher in the field of computer-assisted medical intervention, with a primary focus on deep learning for surgical vision and image-guided procedures. Wang’s most cited work introduces a lightweight attention network designed for the challenging task of guidewire segmentation and localization in clinical fluoroscopic images, specifically during transcatheter arterial chemoembolization (TACE). This contribution is critical for enhancing the precision of robotic systems and assisting physicians in real-time navigation during vascular interventional surgery. By addressing the complex morphology of guidewires in low-contrast X-ray images, Wang’s approach balances computational efficiency with high accuracy, a key requirement for clinical deployment. Although early in their career, with the paper already garnering citations, Wang’s work demonstrates a clear impact on the intersection of surgical robotics and medical image analysis. Their research holds promise for improving the safety and efficacy of minimally invasive procedures, marking them as a notable emerging talent in the field of interventional imaging and AI-driven surgical assistance.
Research Focus
Key Achievements
Top Papers
- 1