Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation
This work addresses brain tumor segmentation for medical imaging, but it is incremental as it adapts existing fusion methods to a new domain.
The authors tackled brain tumor segmentation by proposing a multi-modal CNN approach that adapts fusion methods from video recognition, finding that learning separate representations for each modality before combining them improved performance on the BRATS dataset.
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.