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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F14%3A00224858" target="_blank" >RIV/68407700:21220/14:00224858 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6889834" target="_blank" >http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6889834</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IJCNN.2014.6889834" target="_blank" >10.1109/IJCNN.2014.6889834</a>

Result language
angličtina
Original language name
Study of Learning Entropy for Novelty Detection in lung tumor motion prediction for target tracking radiation therapy
Original language description
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 inreal 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.
Czech name
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Czech description
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Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Neural Networks (IJCNN), 2014 International Joint Conference on - Scopus ISBN
ISBN
978-1-4799-1484-5
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
3124-3129
Publisher name
IEEE
Place of publication
Piscataway
Event location
Beijing
Event date
Jul 6, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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