  In this work, we propose a multi-modal Convolutional Neural Network (CNN)
approach for brain tumor segmentation. We investigate how to combine different
modalities efficiently in the CNN framework.We adapt various fusion methods,
which are previously employed on video recognition problem, to the brain tumor
segmentation problem,and we investigate their efficiency in terms of memory and
performance.Our experiments, which are performed on BRATS dataset, lead us to
the conclusion that learning separate representations for each modality and
combining them for brain tumor segmentation could increase the performance of
CNN systems.
