Jointly trained machine learning models for automatic contouring in radiotherapy applications

Abstract

Joint training techniques to train multiple models across clinical datasets for automatic contouring. Rather than using separate deep neural networks that are trained independently for each different dataset (e.g., a different image contrast or anatomy), joint training can be used to train multiple models simultaneously across clinical datasets for automatic contouring. By taking advantage of commonalities between two or more datasets, the techniques effectively take advantage of data that would otherwise be considered irrelevant to the task—allowing the user to train more performant models while requiring less training data per dataset.