3 accepted papers
Voxel-wise dose prediction is a critical yet challenging task in radiotherapy (RT) planning, as bespoke models trained from scratch often struggle to generalize across institutions, scanners, and planning protocols. Meanwhile, large generative backbones pretrained on billion-scale visual data have l
In contemporary radiotherapy planning (RTP), a key module leaf sequencing is predominantly addressed by optimization-based approaches. In this paper, we propose a novel deep reinforcement learning (DRL) model termed as *Reinforced Leaf Sequencer* (RLS) in a multi-agent framework for leaf sequencing.…
Deep learning has been utilized in knowledge-based radiotherapy planning in which a system trained with a set of clinically approved plans is employed to infer a three-dimensional dose map for a given new patient. However, previous deep methods are primarily limited to simple scenarios, e.g., a fixe…