Pediatric pancreatitis is a progressive and debilitating inflammatory condition, including acute pancreatitis and chronic pancreatitis, that presents significant clinical diagnostic challenges. Machine learning-based methods also face diagnostic challenges due to limited sample availability and multimodal imaging complexity. To address these challenges, this paper introduces Upstream Probabilistic Meta-Imputation (UPMI), a light-weight augmentation strategy that operates upstream of a meta-learner in a low-dimensional meta-feature space rather than in image space. Modality-specific logistic regressions (T1W and T2W MRI radiomics) produce probability outputs that are transformed into a 7-dimensional meta-feature vector. Class-conditional Gaussian mixture models (GMMs) are then fit within each cross-validation fold to sample synthetic meta-features that, combined with real meta-features, train a Random Forest (RF) meta-classifier. On 67 pediatric subjects with paired T1W/T2W MRIs, UPMI achieves a mean AUC of 0.908 ± 0.072, a ∼5% relative gain over a real-only baseline (AUC 0.864 ± 0.061).
@article{arxiv.2511.17635,
title = {Upstream Probabilistic Meta-Imputation for Multimodal Pediatric Pancreatitis Classification},
author = {Max A. Nelson and Elif Keles and Eminenur Sen Tasci and Merve Yazol and Halil Ertugrul Aktas and Ziliang Hong and Andrea Mia Bejar and Gorkem Durak and Oznur Leman Boyunaga and Ulas Bagci},
journal= {arXiv preprint arXiv:2511.17635},
year = {2025}
}