  Automatic brain tumor segmentation from multi-modality Magnetic Resonance
Images (MRI) using deep learning methods plays an important role in assisting
the diagnosis and treatment of brain tumor. However, previous methods mostly
ignore the latent relationship among different modalities. In this work, we
propose a novel end-to-end Modality-Pairing learning method for brain tumor
segmentation. Paralleled branches are designed to exploit different modality
features and a series of layer connections are utilized to capture complex
relationships and abundant information among modalities. We also use a
consistency loss to minimize the prediction variance between two branches.
Besides, learning rate warmup strategy is adopted to solve the problem of the
training instability and early over-fitting. Lastly, we use average ensemble of
multiple models and some post-processing techniques to get final results. Our
method is tested on the BraTS 2020 online testing dataset, obtaining promising
segmentation performance, with average dice scores of 0.891, 0.842, 0.816 for
the whole tumor, tumor core and enhancing tumor, respectively. We won the
second place of the BraTS 2020 Challenge for the tumor segmentation task.
