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Although multi-modal imaging tends to improve the segmentation and classification performance in the field of medical image processing, lacking certain modalities at test time limits its clinical applicability. In this paper, we explored the ability of cross-modal distillation for increasing the performance of T1w MRI-based brain tumor segmentation. More specifically, we considered having high resolution T1w and T2w MRI sequences available for training while having only a high resolution T1w MRI sequence available at test time. We investigated the efficacy of the proposed method to improve the whole tumor segmentation using the BRATS 2018 dataset. Both cross-modal knowledge distillation and cross-modal feature distillation approaches were confirmed to enrich the representation of the T1w MRI sequence by learning from the representation of the more informative T2w MRI sequence during training, thereby improving the mean Dice scores by 6.14 % and 7.02 %, respectively.
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