Masakazu Yoshimura
Papers
2
Total Citations
28
H-Index
2
About
Masakazu Yoshimura is a leading researcher in surgical robotics, with a primary focus on computer vision and pose estimation for minimally invasive procedures. His work addresses critical challenges in robot-assisted surgery, particularly for complex operations such as skull-base tumor removal via transnasal access. Yoshimura’s major contributions include developing advanced algorithms that enable precise, single-shot pose estimation of surgical instrument shafts from monocular endoscopic images—a breakthrough that enhances collision avoidance and patient safety. His 2020 paper on this topic has garnered 22 citations, reflecting its impact on the field. More recently, he introduced MBAPose (2021), a mask and bounding-box aware pose estimation method that leverages photorealistic domain randomization to overcome the degradation of robot control parameters during surgery, addressing a key limitation in real-world surgical settings. Though newer, this work is gaining traction with 6 citations. Yoshimura’s research is notable for its practical focus on improving the accuracy and reliability of surgical robots, directly contributing to safer, more effective minimally invasive procedures. His innovative approaches to domain randomization and monocular estimation position him as a rising authority in surgical robotics and computer vision.
Research Focus
Key Achievements
Top Papers
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- 2