Online RNNs (RTRL, SnAp-1) beat linear filters and transformers at medium-to-long horizon forecasting of PCA respiratory motion weights in two cine-MRI datasets, yielding sub-1.4 mm and sub-2.8 mm geometric errors.
thesis, The University of Tokyo, 2016.URL: https://repository.dl.itc.u-tokyo.ac.jp/record/48459/ files/A32580_summary.pdf
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Frame forecasting in cine MRI using the PCA respiratory motion model: comparing recurrent neural networks trained online and transformers
Online RNNs (RTRL, SnAp-1) beat linear filters and transformers at medium-to-long horizon forecasting of PCA respiratory motion weights in two cine-MRI datasets, yielding sub-1.4 mm and sub-2.8 mm geometric errors.