Upstream Probabilistic Meta-Imputation for Multimodal Pediatric Pancreatitis Classification

• URL: http://arxiv.org/abs/2511.17635v1
• Date: Wed, 19 Nov 2025 07:47:39 GMT
• Title: Upstream Probabilistic Meta-Imputation for Multimodal Pediatric Pancreatitis Classification
• Abstract summary: 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.<n>On 67 pediatric subjects with paired T1W/T2W MRIs, UPMI achieves a mean AUC of 0.908 $pm$ 0.072, a $sim$5% relative gain over a real-only baseline.
• Score: 4.512640350507581
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $\pm$ 0.072, a $\sim$5% relative gain over a real-only baseline (AUC 0.864 $\pm$ 0.061).

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