This survey provides the first comprehensive overview of deep multimodal learning methods designed to remain robust when some input modalities are absent.
Causal debiasing medical multimodal representation learning with missing modalities
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
A dual-stream neural network framework disentangles causal features from spurious correlations in healthcare multimodal data via generalized cross-entropy loss and mutual information minimization, yielding consistent gains on MIMIC-IV, eICU, and ADNI.
citing papers explorer
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Deep Multimodal Learning with Missing Modality: A Survey
This survey provides the first comprehensive overview of deep multimodal learning methods designed to remain robust when some input modalities are absent.
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Handling and Interpreting Missing Modalities in Patient Clinical Trajectories via Autoregressive Sequence Modeling
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.
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Robust Multimodal Representation Learning in Healthcare
A dual-stream neural network framework disentangles causal features from spurious correlations in healthcare multimodal data via generalized cross-entropy loss and mutual information minimization, yielding consistent gains on MIMIC-IV, eICU, and ADNI.