Study on predicting tumor motion via memory-based learning

摘要：

　　Prediction is necessary to compensate system latency in real-time tracking radiotherapy for thoracic and abdominal cancers.The paper proposes a memory-based learning method to predict respiratory tumor motion.The method first stores the training data in memory and then finds relevant data to answer a particular query.By fitting relatively simple models to local patches instead of fitting one single global model,it is able to capture highly nonlinear and complex relations between the internal tumor motion and external surrogates accurately and immediately.Furthermore,due to the local nature of weighting functions,it is inherently robust to outliers in the training data.

关键词： tumor motion respiratory surrogate prediction memory-based learning

作者: Weiquan Wan Zhiqiang He Chaomin Chen

作者单位: Institute of Biomedical Engineering,Southern Medical University,China

会议类型: 国际会议

会议名称: 2013 2nd international Conference on Opto-Electronics Engineering and Materials Eesearch(2013第二届光电工程与材料研究国际会议)(OEMR2013)

会议地点: 郑州

会议语种:英文

页码: 979-982

在线出版日期: 2013-10-19（万方平台首次上网日期，不代表论文的发表时间）