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Found 3 papers, 2 papers with code
Respiratory motion forecasting with online learning of recurrent neural networks for safety enhancement in externally guided radiotherapy
We use UORO, SnAp-1, and DNI to forecast each marker's 3D position with horizons (the time interval in advance for which the prediction is made) h<=2. 1s and compare them with RTRL, least mean squares, and linear regression.
Prediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapy
The amplitude of the motion of the tracked points ranged from 12. 0mm to 22. 7mm.
Prediction of the Position of External Markers Using a Recurrent Neural Network Trained With Unbiased Online Recurrent Optimization for Safe Lung Cancer Radiotherapy
Prediction with online learning of recurrent neural networks (RNN) allows for adaptation to non-stationary respiratory signals, but classical methods such as RTRL and truncated BPTT are respectively slow and biased.
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Multivariate Time Series Forecasting
on ExtMarker
