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This paper proposes CL-DMDF, a dynamic multimodal data fusion model that uses contrastive learning and a dual-dimensional attention mechanism to handle missing modalities and improve discriminative learning.
This paper proposes a graph-based one-stage framework for brain tumor segmentation that handles missing MRI modalities by introducing modality-specific virtual nodes and a dynamic connection strategy, outperforming state-of-the-art methods on the BRATS-2018 and BRATS-2020 datasets.
MuteBench is a benchmark for evaluating multimodal fusion models under modality missing and within-modality missing conditions across clinical datasets. It provides insights into architecture robustness and suggests that diffusion-based imputation can help.
This paper proposes FedMPO, a robust federated multimodal graph learning method that addresses modality heterogeneity and missing modalities through topology-aware cross-modal generation, missing-aware expert routing, and reliability-aware aggregation, achieving performance gains on multiple datasets.