  We propose the BayesDose-Framework, a Bayesian approach for fast and accurate
dose prediction in proton therapy. Our framework is based on a previously
published deterministic LSTM model and is trained and evaluated on simulated
beamlet doses from water phantoms and patient geometries. We parameterize the
network's weights using 2D Gaussian mixture models and use ensemble predictions
to quantify mean dose predictions and their standard deviation. The BayesDose
model performs similarly to the deterministic variant. The uncertainty
predictions are conservative but correlate well spatially and in magnitude with
dose differences. This correlation is reduced when applied to patient data with
unseen relative stopping power value ranges, which could be successfully
addressed by re-training. We parallelize predictions and presample network
weights to reduce runtime overhead. Bayesian models like BayesDose can provide
fast predictions with quality equal to deterministic models and may support
decision making and quality assurance in clinical settings in the future.
