Zi-Ying Luo

Personalized Treatment Outcome Prediction from Scarce Data via Dual-Channel Knowledge Distillation and Adaptive Fusion

A cross-fidelity knowledge distillation and adaptive fusion network (CFKD-AFN), which leverages abundant but low-fidelity simulation data to enhance the prediction on scarce but high-fidelity trial data, and is extended to an interpretable variant for exploratory analysis of feature-attribution patterns associated with treatment outcomes.