Xiaokun Liang
Papers
1
Total Citations
12
H-Index
1
About
Xiaokun Liang is a researcher at the forefront of medical physics and artificial intelligence, with a primary focus on real-time respiratory motion management in radiotherapy. His most significant contribution is the development of LSTformer (Long Short-Term Transformer), a novel deep learning architecture that addresses the critical challenge of predicting tumor movement during clinical surgery. By integrating the strengths of Transformers with long short-term memory mechanisms, Liang’s work overcomes the limitations of traditional RNN-based respiratory management methods, enabling more accurate and real-time predictions of breathing patterns. This innovation directly improves the efficacy of radiotherapy by compensating for tumor motion, a key factor in treatment precision. With his 2022 paper on LSTformer garnering 12 citations, Liang’s research is gaining traction in the medical AI community. His work stands out for its practical impact, offering a robust solution to a long-standing clinical problem. Liang’s contributions are paving the way for safer, more effective cancer treatments, marking him as an emerging leader in the intersection of deep learning and radiation oncology.
Research Focus
Key Achievements
Top Papers
- 1LSTformer: Long Short-Term Transformer for Real Time Respiratory Prediction12 citations · 2022