{"ID":2839433,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.17635","arxiv_id":"2511.17635","title":"Upstream Probabilistic Meta-Imputation for Multimodal Pediatric Pancreatitis Classification","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).","short_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 compl...","url_abs":"https://arxiv.org/abs/2511.17635","url_pdf":"https://arxiv.org/pdf/2511.17635v1","authors":"[\"Max A. Nelson\",\"Elif Keles\",\"Eminenur Sen Tasci\",\"Merve Yazol\",\"Halil Ertugrul Aktas\",\"Ziliang Hong\",\"Andrea Mia Bejar\",\"Gorkem Durak\",\"Oznur Leman Boyunaga\",\"Ulas Bagci\"]","published":"2025-11-19T07:47:39Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\"]","methods":"[]","has_code":false}