MuteBench evaluates multimodal fusion robustness to modality missing and within-modality missing on 125000 samples from 9 clinical datasets, finding architecture family predicts tolerance better than parameter count.
DrFuse: Learning disentangled representation for clinical multi-modal fusion with missing modality and modal inconsistency
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RePercENT introduces a plug-and-play self-supervised framework for scalable pairwise disentangled representation learning across more than two modalities using pre-extracted embeddings and a joint optimization objective with theoretical optimality guarantees.
Autoregressive transformer modeling with missingness-aware contrastive pre-training outperforms baselines on MIMIC-IV and eICU benchmarks and mitigates divergent behavior from removed modalities in clinical trajectories.
Autoregressive LLM decoders with a missingness-aware contrastive pre-training objective outperform static baselines on MIMIC-IV/eICU and reveal demographic-bias failure modes under modality ablation.
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A Scientific Human-Agent Reproduction Pipeline
Autoregressive LLM decoders with a missingness-aware contrastive pre-training objective outperform static baselines on MIMIC-IV/eICU and reveal demographic-bias failure modes under modality ablation.