ArXiv

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

2026-05-08 · 17:35 UTC ·Yi Yu, Parker Martin, Zhenyu Bu... ·1 min read
Authors
Yi Yu, Parker Martin, Zhenyu Bu...
Categories
cs.CL
arXiv
https://arxiv.org/abs/2605.08045v1
PDF
https://arxiv.org/pdf/2605.08045v1

Brief

CMR-EXTR converts free-text cardiac magnetic resonance (CMR) reports into auditable structured data with per-field confidence. The method employs a teacher–student distillation pipeline for fully offline inference and reduced annotation effort, plus an uncertainty model that blends distribution plausibility, sampling stability, and cross-field consistency to prioritize human review. It achieves 99.65% variable-level accuracy; code on GitHub; accepted to ISBI 2026.

Why it matters

CMR-EXTR converts free-text cardiac magnetic resonance (CMR) reports into auditable structured data and provides per-field confidence; evaluated results report 99.65% variable-level accuracy (ArXiv: 2026-05-08; authors include Yi Yu and Parker Martin).

Key details

  • The system uses a teacher–student distillation pipeline to enable fully offline inference with limited manual annotation and an uncertainty scheme combining distribution plausibility, sampling stability, and cross-field consistency to triage human review.
  • Authors claim this is the first CMR-specific extraction system with integrated confidence estimation; code is available at https://github.com/yuyi1005/CMR-EXTR and the work was accepted to ISBI 2026.
Source evidence

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.

Comment: Accepted to ISBI 2026