A class-conditional Gaussian augmentation of log-probability meta-features modestly regularizes tree-based stacking combiners for IPMN risk stratification, while fold-locked fusion of radiomics and 2.5D CNN streams achieves the best overall discrimination (QWK 0.595).
Nehra, Mark G
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Gaussian Meta-Space Augmentation for Stacking Ensembles in Multimodal IPMN Risk Stratification
A class-conditional Gaussian augmentation of log-probability meta-features modestly regularizes tree-based stacking combiners for IPMN risk stratification, while fold-locked fusion of radiomics and 2.5D CNN streams achieves the best overall discrimination (QWK 0.595).