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  • This paper presents a study of lung tumormotion time-series prediction, first, with the use of conventional static (feedforward) MLP neural network (with a single hidden perceptron layer) and, second, with the static quadratic neural unit (QNU), i.e., a class of polynomial neural network (or a higher-order neural unit). We also demonstrate that QNU can be trained in a very efficient and fast way for real time retraining due to its linear nature of optimization problem. The objective is the prediction accuracy of 1 [mm] for 1-second prediction horizon. So it is well applicable for radiation tracking therapy.
  • This paper presents a study of lung tumormotion time-series prediction, first, with the use of conventional static (feedforward) MLP neural network (with a single hidden perceptron layer) and, second, with the static quadratic neural unit (QNU), i.e., a class of polynomial neural network (or a higher-order neural unit). We also demonstrate that QNU can be trained in a very efficient and fast way for real time retraining due to its linear nature of optimization problem. The objective is the prediction accuracy of 1 [mm] for 1-second prediction horizon. So it is well applicable for radiation tracking therapy. (en)
Title
  • Lung Tumor Motion Prediction by static neural networks
  • Lung Tumor Motion Prediction by static neural networks (en)
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  • Lung Tumor Motion Prediction by static neural networks
  • Lung Tumor Motion Prediction by static neural networks (en)
skos:notation
  • RIV/68407700:21220/12:00198033!RIV13-MSM-21220___
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  • 147602
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  • RIV/68407700:21220/12:00198033
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  • Lung tumor-motion; time series prediction; radiation therapy; MLP; QNU; retraining (en)
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  • [1DE523E1D7C4]
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  • Bíla, Jiří
  • Bukovský, Ivo
  • Homma, N.
  • Rodríguez Jorge, Ricardo
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  • 21220
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