  We explore encoding brain symmetry into a neural network for a brain tumor
segmentation task. A healthy human brain is symmetric at a high level of
abstraction, and the high-level asymmetric parts are more likely to be tumor
regions. Paying more attention to asymmetries has the potential to boost the
performance in brain tumor segmentation. We propose a method to encode brain
symmetry into existing neural networks and apply the method to a
state-of-the-art neural network for medical imaging segmentation. We evaluate
our symmetry-encoded network on the dataset from a brain tumor segmentation
challenge and verify that the new model extracts information in the training
images more efficiently than the original model.
