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About

Miaojing Shi is a leading researcher at the intersection of computer vision and surgical robotics, with a primary focus on advancing minimally invasive surgery through intelligent image analysis. Her work centers on surgical instrument segmentation, a critical technology for computer-assisted interventions. Shi’s major contributions include pioneering text-promptable segmentation methods that allow surgeons to query specific instruments using natural language, overcoming challenges of instrument diversity and differentiation. She also developed SegMatch, a semi-supervised learning approach that dramatically reduces the need for expensive manual annotations in laparoscopic and robotic surgery datasets. Her most impactful work includes the creation of CholecInstanceSeg, a comprehensive tool instance segmentation dataset for laparoscopic surgery, which has already garnered 12 citations since its 2025 release. With over 30 total citations across her top papers, Shi’s research is recognized for bridging the gap between clinical needs and cutting-edge AI, enabling more precise, adaptive, and accessible surgical assistance technologies that promise to improve patient outcomes and surgical training.