Yanfang Li

Changchun University of Science and Technology

Papers

Total Citations

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About

Yanfang Li’s research bridges the critical intersection of artificial intelligence and advanced sensing technologies, with a primary focus on surgical phase recognition and high-performance magnetic sensors. In her most cited work, “SE-OHFM: A Surgical Phase Recognition Network with SE Attention Module” (2021, 10 citations), Li introduced an innovative deep neural network that leverages squeeze-and-excitation attention mechanisms to automatically identify surgical phases in robot-assisted minimally invasive surgery. This contribution directly addresses the growing need for enhanced context awareness in the operating room, aiming to improve surgeon performance and patient safety. Building on this foundation, Li’s more recent work, “Dual-mode Low Noise Large Range Magnetic Sensor Based on Giant Magnetoimpedance Effect” (2024, 2 citations), demonstrates her versatility by tackling challenges in magnetic sensing for applications spanning navigation, robotics, and medical equipment. This dual-mode sensor achieves both a large detection range and low noise, offering a practical solution for increasingly demanding performance requirements. Li’s research reflects a commitment to translating computational methods into real-world clinical and industrial tools, making her a promising voice in the fields of medical AI and sensor engineering.