Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation
This addresses a practical issue in medical imaging where data variability across sites leads to incomplete modality availability, improving segmentation accuracy for brain tumor diagnosis.
The paper tackles the problem of missing imaging modalities in brain tumor segmentation by proposing a meta-learning approach to enhance modality-agnostic representations, even with limited full modality samples, and reports that it significantly outperforms state-of-the-art techniques in missing modality scenarios.
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.