MoRE-Net: An Interpretable and Modality-robust Model for Brain Tumor Grading

Interpretability and robustness are both critical for developing trustworthy artificial intelligence, especially in high-stakes domains such as medical diagnosis. However, few studies have explored how to enhance robustness within interpretable model frameworks. This work aims to improve the robustness of interpretable multimodal medical imaging diagnostic models, particularly under missing modality conditions.

We propose the Modality-Robust and Explainable Network (MoRE-Net), a robust and interpretable model for brain tumor grading. Built on a variant of the interpretable prototypical part network, MoRE-Net uses independent per-modality encoders to extract modality-specific features. To address the absence of inter-modality interactions, we introduce 2 key designs: (1) Mamba-based per-modality encoders for efficient global-context modeling; and (2) an online multimodal teacher that guides the per-modality encoders via an alignment loss during early training, which is gradually annealed and removed. We evaluate MoRE-Net on 369 subjects with multimodal MRI from the BraTS2020 dataset, using balanced accuracy (BAC) for grading performance and activation precision (AP) for interpretability. We further validate the model on the real-world ReMIND dataset.

MoRE-Net achieves an average BAC of 73.5% and AP of 61.2% across all missing modality scenarios on BraTS2020 dataset, surpassing baseline methods by about 15% and 21%, respectively. Results on ReMIND dataset and ablation studies confirm its effectiveness of each proposed strategy and the overall robustness.

We introduce MoRE-Net, a novel interpretable and modality-robust model for brain tumor grading. Experimental results demonstrate its strong performance in both diagnostic accuracy and interpretability under missing modality conditions, indicating its potential for clinical deployment.