Lung Tumor Motion Prediction by static neural networks

Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F12%3A00198033" target="_blank" >RIV/68407700:21220/12:00198033 - isvavai.cz</a>
Výsledek na webu
—
DOI - Digital Object Identifier
—

Jazyk výsledku
angličtina
Název v původním jazyce
Lung Tumor Motion Prediction by static neural networks
Popis výsledku v původním jazyce
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., aclass 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.
Název v anglickém jazyce
Lung Tumor Motion Prediction by static neural networks
Popis výsledku anglicky
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., aclass 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.

Rok uplatnění
2012
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Podobné výsledky(10)