# Upstream Probabilistic Meta-Imputation for Multimodal Pediatric Pancreatitis Classification

arXiv: https://arxiv.org/abs/2511.17635 · HTML: https://mlsanity.com/paper/2511.17635/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 19 Nov 2025.

## Builds on (most cited first, 5 of 5)

- [GANs for Medical Image Synthesis: An Empirical Study](https://mlsanity.com/paper/2105.05318/) (2021)
- [Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review](https://mlsanity.com/paper/2307.13125/) (2023)
- [Synthetic Data in Radiological Imaging: Current State and Future Outlook](https://mlsanity.com/paper/2407.01561/) (2024)
- [Radiomics Boosts Deep Learning Model for IPMN Classification](https://mlsanity.com/paper/2309.05857/) (2023)
- [Pediatric Pancreas Segmentation from MRI Scans with Deep Learning](https://mlsanity.com/paper/2506.15908/) (2025)

## Abstract (arXiv)

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).