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

doi:10.1109/IJCNN.2014.6889834

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

MED - Health Sciences

Czech Technical University in Prague

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3124-3129
Number of pages	6
ISBN (Electronic)	9781479914845
DOIs	https://doi.org/10.1109/IJCNN.2014.6889834
Publication status	Published - 2014 Sept 3
Event	2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China Duration: 2014 Jul 6 → 2014 Jul 11

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Conference

Conference	2014 International Joint Conference on Neural Networks, IJCNN 2014
Country/Territory	China
City	Beijing
Period	14/7/6 → 14/7/11

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10.1109/IJCNN.2014.6889834

Cite this

Bukovsky, I., Homma, N., Cejnek, M., & Ichiji, K. (2014). Study of Learning Entropy for Novelty Detection in lung tumor motion prediction for target tracking radiation therapy. In Proceedings of the International Joint Conference on Neural Networks (pp. 3124-3129). Article 6889834 (Proceedings of the International Joint Conference on Neural Networks). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2014.6889834

Bukovsky, Ivo ; Homma, Noriyasu ; Cejnek, Matous et al. / Study of Learning Entropy for Novelty Detection in lung tumor motion prediction for target tracking radiation therapy. Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 3124-3129 (Proceedings of the International Joint Conference on Neural Networks).

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

abstract = "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.",

author = "Ivo Bukovsky and Noriyasu Homma and Matous Cejnek and Kei Ichiji",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 International Joint Conference on Neural Networks, IJCNN 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

year = "2014",

month = sep,

day = "3",

doi = "10.1109/IJCNN.2014.6889834",

language = "English",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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}

Bukovsky, I, Homma, N, Cejnek, M & Ichiji, K 2014, Study of Learning Entropy for Novelty Detection in lung tumor motion prediction for target tracking radiation therapy. in Proceedings of the International Joint Conference on Neural Networks., 6889834, Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc., pp. 3124-3129, 2014 International Joint Conference on Neural Networks, IJCNN 2014, Beijing, China, 14/7/6. https://doi.org/10.1109/IJCNN.2014.6889834

Study of Learning Entropy for Novelty Detection in lung tumor motion prediction for target tracking radiation therapy. / Bukovsky, Ivo; Homma, Noriyasu; Cejnek, Matous et al.
Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc., 2014. p. 3124-3129 6889834 (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Bukovsky, Ivo

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AU - Cejnek, Matous

AU - Ichiji, Kei

PY - 2014/9/3

Y1 - 2014/9/3

N2 - 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.

AB - 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.

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M3 - Conference contribution

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T3 - Proceedings of the International Joint Conference on Neural Networks

SP - 3124

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BT - Proceedings of the International Joint Conference on Neural Networks

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Bukovsky I, Homma N, Cejnek M, Ichiji K. Study of Learning Entropy for Novelty Detection in lung tumor motion prediction for target tracking radiation therapy. In Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc. 2014. p. 3124-3129. 6889834. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN.2014.6889834

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

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