Continuous-Time Deep Glioma Growth Models

@article{Petersen2021ContinuousTimeDG,
  title={Continuous-Time Deep Glioma Growth Models},
  author={Jens Petersen and Fabian Isensee and Gregor Koehler and Paul F. Jager and David Zimmerer and Ulf Neuberger and Wolfgang Wick and J{\"u}rgen Debus and Sabine Heiland and Martin Bendszus and Philipp Vollmuth and Klaus H. Maier-Hein},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.12917}
}
The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation therapy. Recent work has approached the glioma growth modeling problem via deep learning and variational inference, thus learning growth dynamics entirely from a real patient data distribution. So far, this approach was constrained to predefined image acquisition intervals and sequences of fixed length, which limits… 
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