Yaoqin Xie

Papers

Total Citations

H-Index

About

Yaoqin Xie is a leading researcher at the intersection of medical physics and artificial intelligence, with key contributions to real-time respiratory motion management in radiotherapy and AI-driven acupuncture point detection. His work addresses a critical challenge in cancer treatment: compensating for tumor movement caused by patient breathing during radiotherapy. In his highly cited 2022 paper "LSTformer: Long Short-Term Transformer for Real Time Respiratory Prediction" (12 citations), Xie introduced a novel deep learning architecture that outperforms traditional RNN-based methods for predicting respiratory motion, enabling more precise radiation delivery. His foundational 2014 study on pancreas motion during CyberKnife radiotherapy (6 citations) established essential acquisition frequency parameters for tracking intrafractional organ movement, directly improving treatment accuracy for pancreatic cancer patients. Most recently, Xie's 2024 work "Rt-Demt: A Hybrid Real-Time Acupoint Detection Model Combining Mamba and Transformer" (4 citations) extends his expertise in real-time prediction to traditional medicine, demonstrating his versatility in applying advanced sequence modeling to diverse biomedical problems. With over 20 years of research experience, Xie continues to bridge computational innovation and clinical application, making radiotherapy safer and more effective for cancer patients worldwide.