Yiqi Huang
Papers
2
Total Citations
6
H-Index
2
About
Yiqi Huang is a rising researcher in the field of computer vision for minimally invasive surgery, with a focus on laparoscopic scene understanding. Their work bridges stereo matching and surgical instrument segmentation—two critical tasks for enabling robotic-assisted surgery and augmented reality guidance. Huang’s most-cited paper, "Laparoscopic stereo matching using 3-Dimensional Fourier transform with full multi-scale features" (2024, 4 citations), introduces a novel method that leverages frequency-domain analysis to improve depth estimation in complex surgical environments. Building on this, "MCF-SMSIS: Multi-tasking with complementary functions for stereo matching and surgical instrument segmentation" (2024, 2 citations) demonstrates a unified framework that jointly addresses both tasks, enhancing efficiency and accuracy. Though early in their career, Huang’s work has already attracted attention for its innovative integration of multi-scale features and task complementarity. Their contributions are particularly notable for addressing real-world challenges in laparoscopy, such as tissue deformation and instrument occlusion. As the demand for intelligent surgical systems grows, Huang’s research promises to play a key role in advancing autonomous and semi-autonomous surgical tools.
Research Focus
Key Achievements
Top Papers
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- 2