Multimodal Fusion: Incremental Value Explorer

Fuse a clinical model and an imaging model by weighted averaging, and test whether the combination actually beats the stronger baseline.

multimodal
fusion
incremental value
ROC

ROC curves: clinical vs. imaging vs. fusion

Clinical only (AUC=0.95)Imaging only (AUC=0.97)Fusion (AUC=0.99)

Metrics table

Model	AUC	Brier
Clinical only	0.952	0.114
Imaging only	0.968	0.095
Fusion	0.994	0.091

Clinical vs. imaging predicted probabilities

Agreement is low; fusion only helps if errors are uncorrelated.

Incremental value depends on independent errors

Fusion beats both single-modality models only when the clinical and imaging scores make largely uncorrelated mistakes — that is what the scatter plot's spread away from the diagonal represents. On the internal set a modest imaging weight can lift AUC above either baseline. Switch to the external set: the imaging model typically degrades more than the clinical model under acquisition shift, so the same fusion weight can now hurt, and the optimal weight shifts back toward the clinical-only baseline. Incremental value should always be judged relative to the strongest single-modality model, not against no model at all.

Preparing the simulation…