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Multimodal fusion combines several kinds of data about one patient (slides, expression, mutations, clinical variables) in one model; modality dropout randomly hides modalities during training so the model still works when some are missing.
Multimodal learning integrates and processes multiple data types, or modalities, in one deep model (Wikipedia). Patient data is rarely complete, so fusion models mask absent modalities in attention and use modality dropout, an application of dropout regularisation (Wikipedia), to stay robust. PORPOISE fused histology and genomics for pan-cancer prognosis; the recurring finding is that when one modality (expression) already carries the signal, fusion adds little, and the honest test is each modality alone against the combination.
Shares Contrastive learning (InfoNCE), Principal component analysis (PCA) as a feature compressor, Transformer and attention, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Ablation study and multi-task heads, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Ablation study and multi-task heads, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Transformer and attention, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Contrastive learning (InfoNCE), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Principal component analysis (PCA) as a feature compressor, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Transformer and attention, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Principal component analysis (PCA) as a feature compressor, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.