Papers
1
Total Citations
18
H-Index
1
About
Sen Yang is an emerging researcher at the intersection of computer vision and surgical robotics, with a focused expertise in intelligent surgical tool recognition and medical image analysis. His most notable contribution, published in 2023, demonstrates the application and rigorous evaluation of Convolutional Neural Network (CNN)-based frameworks for recognizing surgical tools and their precise tip locations across multiple endoscopic surgical environments — a technically demanding challenge with direct implications for autonomous robotic surgery and computer-assisted interventions. This work, which has already garnered 18 citations within a short timeframe, addresses a critical bottleneck in surgical AI: the need for robust, generalizable tool detection systems that perform reliably across diverse clinical scenarios rather than controlled laboratory settings. By validating CNN architectures in real-world endoscopic contexts, Yang's research bridges the gap between deep learning theory and practical surgical application, offering a meaningful step toward safer, more intelligent operating room technologies. His work positions him as a promising contributor to the rapidly growing field of surgical data science, where accurate instrument tracking remains foundational to advancing minimally invasive procedures and intraoperative decision support systems.
Research Focus
Key Achievements
Top Papers
- 1