  Liver cancer is one of the most common cancers worldwide. Due to
inconspicuous texture changes of liver tumor, contrast-enhanced computed
tomography (CT) imaging is effective for the diagnosis of liver cancer. In this
paper, we focus on improving automated liver tumor segmentation by integrating
multi-modal CT images. To this end, we propose a novel mutual learning (ML)
strategy for effective and robust multi-modal liver tumor segmentation.
Different from existing multi-modal methods that fuse information from
different modalities by a single model, with ML, an ensemble of
modality-specific models learn collaboratively and teach each other to distill
both the characteristics and the commonality between high-level representations
of different modalities. The proposed ML not only enables the superiority for
multi-modal learning but can also handle missing modalities by transferring
knowledge from existing modalities to missing ones. Additionally, we present a
modality-aware (MA) module, where the modality-specific models are
interconnected and calibrated with attention weights for adaptive information
exchange. The proposed modality-aware mutual learning (MAML) method achieves
promising results for liver tumor segmentation on a large-scale clinical
dataset. Moreover, we show the efficacy and robustness of MAML for handling
missing modalities on both the liver tumor and public brain tumor (BRATS 2018)
datasets. Our code is available at https://github.com/YaoZhang93/MAML.
