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Deep learning methods for brain tumor segmentation are typically trained inan ad hoc fashion on all available data. Brain tumors are tremendouslyheterogeneous in image appearance and labeled training data is limited. Weargue that incorporation of additional prior information, specifically tumorgrade, associated with tumor imaging phenotypes during model training cansignificantly improve segmentation performance. Two strategies forincorporation of tumor grade during model training are proposed and theirimpact on segmentation performance is demonstrated on the BRATS 2018 dataset.

Stratify or Inject: Two Simple Training Strategies to Improve Brain Tumor Segmentation