Keywords: Segmentation, BrainAutomated brain tumor segmentation with multi-modal magnetic resonance imaging (MRI) data is crucial for brain cancer diagnosis. Nevertheless, in clinical applications, it is difficult to guarantee that complete multi-modal MRI data are available due to different imaging protocols and inevitable data corruption. A large test time performance drop could happen. Here, we design a modality-adaptive network learning method to extract common representations from different modalities and make our trained model applicable to different data-missing scenarios. Experiments on an open-source dataset demonstrate that our method can reduce the dependence of deep learning-based segmentation methods on the integrity of input data.
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