Hui Che
Papers
1
Total Citations
6
H-Index
1
About
Hui Che is a researcher whose work sits at the intersection of medical imaging, computer vision, and surgical guidance. Their key research areas include ultrasound image analysis, motion prediction, and deep learning for image-guided interventions. Che’s most notable contribution is the development of the Feature Pyramid Self-Attention Network, a novel architecture designed to predict respiratory motion in real-time during ultrasound-guided surgery. This work addresses a critical challenge in minimally invasive procedures: compensating for patient movement caused by breathing. By integrating multi-scale feature extraction with self-attention mechanisms, Che’s model achieves robust and accurate motion forecasting, directly improving the safety and precision of surgical navigation. With over 6 citations on this single paper, their research has already begun to influence the field of computer-assisted surgery. Che’s work stands out for its practical focus on translating advanced deep learning techniques into clinical tools, making them a rising voice in the development of intelligent, adaptive surgical systems.
Research Focus
Key Achievements
Top Papers
- 1