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Clinical-context-Aware and Patient-adaptive Deformable Image Registration can improve contour usability and dose mapping for adaptive radiotherapy and show that clinically informed, longitudinal registration can improve contour usability and dose mapping for adaptive radiotherapy.
Artificial intelligence has advanced individual radiotherapy tasks, yet these capabilities remain separated across clinical stages, software environments and data modalities. This fragmentation contrasts with the longitudinal radiotherapy workflow from treatment decision-making through follow-up. Here we present RadOnc...
This work proposes BrainSeg, a novel unified framework for CBS by using large-scale datasets spanning the entire lifespan, with adaptability to diverse uni- and multimodal input scenarios without the need for retraining or finetuning, providing flexible and reliable analysis for large-scale neuroimaging studies.
A Mixture of Multicenter Experts (MoME) framework to address AI bias in the medical domain without requiring data sharing across institutions is proposed and validated using a multimodal target volume delineation model for prostate cancer radiotherapy.
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