MGML: Enhancing Brain Tumor Segmentation with Incomplete MRI Data
Published:Dec 30, 2025 01:37
•1 min read
•ArXiv
Analysis
This paper addresses the practical challenge of incomplete multimodal MRI data in brain tumor segmentation, a common issue in clinical settings. The proposed MGML framework offers a plug-and-play solution, making it easily integrable with existing models. The use of meta-learning for adaptive modality fusion and consistency regularization is a novel approach to handle missing modalities and improve robustness. The strong performance on BraTS datasets, especially the average Dice scores across missing modality combinations, highlights the effectiveness of the method. The public availability of the source code further enhances the impact of the research.
Key Takeaways
- •Addresses the practical problem of incomplete multimodal MRI data in brain tumor segmentation.
- •Proposes a novel meta-guided multi-modal learning (MGML) framework.
- •Achieves superior performance on BraTS datasets, particularly with missing modalities.
- •Offers a plug-and-play solution, easily integrable with existing models.
- •Source code is publicly available.
Reference
“The method achieved superior performance compared to state-of-the-art methods on BraTS2020, with average Dice scores of 87.55, 79.36, and 62.67 for WT, TC, and ET, respectively, across fifteen missing modality combinations.”