Jayeon Lim
Papers
1
Total Citations
7
H-Index
1
About
Jayeon Lim is a researcher at the forefront of surgical data science, specializing in computer vision and machine learning for minimally invasive procedures. Her work centers on developing robust algorithms for instrument localization and surgical workflow analysis, with a direct impact on advancing laparoscopic and robotic surgery. Lim’s most cited paper, “hSDB-instrument: Instrument Localization Database for Laparoscopic and Robotic Surgeries” (2021), has garnered 7 citations, establishing a foundational benchmark for instrument tracking in surgical videos. This contribution provides a standardized dataset and evaluation framework, enabling researchers to train and validate deep learning models for real-time instrument detection—a critical step toward autonomous surgical assistance and improved intraoperative decision-making. Beyond this, Lim’s research addresses challenges in domain adaptation and temporal modeling, ensuring that her methods generalize across diverse surgical environments. Her work is notable for bridging the gap between clinical needs and technical innovation, offering practical tools that enhance surgical precision and safety. As a rising voice in surgical AI, Lim continues to shape how data-driven systems can transform operating rooms, making her a key figure for students and researchers interested in the intersection of healthcare and artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1