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arXiv · 2605.08045

Uncertainty-Aware Structured Data Extraction from Full CMR Reports via Distilled LLMs

Yi Yu·Parker Martin·Zhenyu Bu·Yixuan Liu·Yi-Yu Zheng·Orlando Simonetti·Yuchi Han·Yuan Xue
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Abstract

Converting free-text cardiac magnetic resonance (CMR) reports into auditable structured data remains a bottleneck for cohort assembly, longitudinal curation, and clinical decision support. We present CMR-EXTR, a lightweight framework that converts free-text CMR reports into structured data and assigns per-field confidence for quality control. A teacher-student distillation pipeline enables fully offline inference while limiting manual annotation. Uncertainty integrates three complementary principles -- distribution plausibility, sampling stability, and cross-field consistency -- to triage human review. Experiments show that CMR-EXTR achieves 99.65% variable-level accuracy, demonstrating both reliable extraction and informative confidence scores. To our knowledge, this is the first CMR-specific extraction system with integrated confidence estimation. The code is available at https://github.com/yuyi1005/CMR-EXTR.

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Yi Yu, Parker Martin, Zhenyu Bu, Yixuan Liu, Yi-Yu Zheng, Orlando Simonetti, Yuchi Han, Yuan Xue. 2026-05-08. Uncertainty-Aware Structured Data Extraction from Full CMR Reports via Distilled LLMs. https://arxiv.org/abs/2605.08045

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