1 citations · 1 across the 2 of their papers we have counts for
3 papers
Comparing local and global optimization reveals untapped hybrid potentials for machine learning-based inverse planning
Tim Ortkamp, Florian Leininger, Zhongxing Liao +2
Machine learning (ML) models for normal tissue complication probability (NTCP) can be embedded directly into inverse treatment planning, but the resulting optimization problems are…
Direct optimization of the probability of lesion origin in proton treatment planning for low-grade glioma patients
Tim Ortkamp, Habiba Sallem, Semi Harrabi +4
In proton therapy of low-grade glioma (LGG) patients, contrast-enhancing brain lesions (CEBLs) on magnetic resonance imaging are considered predictive of late radiation-induced les…
BayesDose: Comprehensive proton dose prediction with model uncertainty using Bayesian LSTMs
Luke Voss, Ahmad Neishabouri, Tim Ortkamp +2
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…