  Multimodal MR images can provide complementary information for accurate brain
tumor segmentation. However, it's common to have missing imaging modalities in
clinical practice. Since there exists a strong correlation between multi
modalities, a novel correlation representation block is proposed to specially
discover the latent multi-source correlation. Thanks to the obtained
correlation representation, the segmentation becomes more robust in the case of
missing modalities. The model parameter estimation module first maps the
individual representation produced by each encoder to obtain independent
parameters, then, under these parameters, the correlation expression module
transforms all the individual representations to form a latent multi-source
correlation representation. Finally, the correlation representations across
modalities are fused via the attention mechanism into a shared representation
to emphasize the most important features for segmentation. We evaluate our
model on BraTS 2018 datasets, it outperforms the current state-of-the-art
method and produces robust results when one or more modalities are missing.
