Study of Learning Entropy for Novelty Detection in lung tumor motion prediction for target tracking radiation therapy

Ivo Bukovsky, Noriyasu Homma, Matous Cejnek, Kei Ichiji

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

This paper presents recently introduced concept of Learning Entropy (LE) for time series and recalls the practical form of its evaluation in real time. Then, a technique that estimates the increased risk of prediction inaccuracy of adaptive predictors in real time using LE is introduced. On simulation examples using artificial signal and real respiratory time series, it is shown that LE can be used to evaluate the actual validity of the adaptive predicting model of time series in real time. The introduced technique is discussed as a potential approach to the improvement of accuracy of lung tumor tracking radiation therapy.

本文言語English
ホスト出版物のタイトルProceedings of the International Joint Conference on Neural Networks
出版社Institute of Electrical and Electronics Engineers Inc.
ページ3124-3129
ページ数6
ISBN(電子版)9781479914845
DOI
出版ステータスPublished - 2014 9 3
イベント2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
継続期間: 2014 7 62014 7 11

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks

Other

Other2014 International Joint Conference on Neural Networks, IJCNN 2014
CountryChina
CityBeijing
Period14/7/614/7/11

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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