Deep Evidential Learning for Dose Prediction

arXiv:2404.17126v1 Announce Type: new
Abstract: In this work, we present a novel application of an uncertainty-quantification framework called Deep Evidential Learning in the domain of radiotherapy dose prediction. Using medical images of the Open Knowledge-Based Planning Challenge dataset, we found that this model can be effectively harnessed to yield uncertainty estimates that inherited correlations with prediction errors upon completion of network training. This was achieved only after reformulating the original loss function for a stable implementation. We found that (i)epistemic …

abstract application arxiv challenge correlations cs.ai cs.lg dataset domain eess.iv found framework images knowledge medical novel physics.med-ph planning prediction quantification type uncertainty work