  In medical vision, different imaging modalities provide complementary
information. However, in practice, not all modalities may be available during
inference or even training. Previous approaches, e.g., knowledge distillation
or image synthesis, often assume the availability of full modalities for all
patients during training; this is unrealistic and impractical due to the
variability in data collection across sites. We propose a novel approach to
learn enhanced modality-agnostic representations by employing a meta-learning
strategy in training, even when only limited full modality samples are
available. Meta-learning enhances partial modality representations to full
modality representations by meta-training on partial modality data and
meta-testing on limited full modality samples. Additionally, we co-supervise
this feature enrichment by introducing an auxiliary adversarial learning
branch. More specifically, a missing modality detector is used as a
discriminator to mimic the full modality setting. Our segmentation framework
significantly outperforms state-of-the-art brain tumor segmentation techniques
in missing modality scenarios.
