More Should Be Better: Incremental Self-Distillation Multimodal Brain Tumor Segmentation with Missing Modalities

Multimodal magnetic resonance imaging provides complementary evidence for brain tumor segmentation, yet one or more modalities are frequently unavailable in clinical practice. A practical model must therefore remain effective for any available modality subset. Existing missing-modality methods largely optimize robustness to modality removal, but overlook the converse question: does the model reliably benefit when an additional modality becomes available? We argue that robustness alone is insufficient and introduce the principle of non-negative modality utility, i.e., enriching a valid modality subset should not worsen its information content for segmentation. Based on this principle, we propose a complementarity-guided incremental self-distillation framework. Specifically, it reformulates robust multimodal representation learning with missing modalities as a progressive multimodal self-distillation problem. Based on the non-negative modality-utility hypothesis, we organize modality subsets into a distillation path along which modality utility progressively increases, allowing representations learned from fewer modalities to gradually approach the representational capacity of the complete-modality setting. Compared with directly learning a joint representation from a restricted modality subset, our incremental self-distillation strategy better preserves modality-specific discriminative information and improves adaptation to diverse missing-modality configurations. Extensive experiments across diverse missing-modality settings demonstrate the effectiveness and robustness of the proposed framework.