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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.08045 |
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| _version_ | 1866918489907789824 |
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| author | Yu, Yi Martin, Parker Bu, Zhenyu Liu, Yixuan Zheng, Yi-Yu Simonetti, Orlando Han, Yuchi Xue, Yuan |
| author_facet | Yu, Yi Martin, Parker Bu, Zhenyu Liu, Yixuan Zheng, Yi-Yu Simonetti, Orlando Han, Yuchi Xue, Yuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08045 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Uncertainty-Aware Structured Data Extraction from Full CMR Reports via Distilled LLMs Yu, Yi Martin, Parker Bu, Zhenyu Liu, Yixuan Zheng, Yi-Yu Simonetti, Orlando Han, Yuchi Xue, Yuan Computation and Language 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. |
| title | Uncertainty-Aware Structured Data Extraction from Full CMR Reports via Distilled LLMs |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2605.08045 |