Interpretable Neural Additive Image Model for Analyzing Image Effects on Numerical Outcomes

The proposed Neural Additive Image Model (NAIM) seamlessly incorporates tabular data and images while preserving global interpretability through additivity constraints. It enables comprehensive exploration and understanding of the impact of various image characteristics on numerical outcomes.