Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation

TL;DR

This paper introduces a meta-learning based method to improve brain tumor segmentation by learning modality-agnostic representations, effectively handling missing modalities during training and inference, with auxiliary adversarial supervision.

Contribution

It proposes a novel meta-learning approach combined with adversarial co-supervision to enhance modality-agnostic features for brain tumor segmentation.

Findings

Outperforms state-of-the-art methods in missing modality scenarios

Effective learning from limited full modality samples

Improves segmentation accuracy with incomplete modality data

Abstract

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.