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Model: yuyi1005/cmrextr-1b
Source: Original Platform

README.md

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+---
+base_model:
+- meta-llama/Llama-3.2-1B-Instruct
+license: mit
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- medical
+- clinical-nlp
+- information-extraction
+- cardiology
+- cmr
+---
+
+# CMR-EXTR: Structured Extraction from Cardiac MRI Reports
+
+CMR-EXTR is a lightweight framework for converting free-text cardiac magnetic resonance (CMR) reports into structured, auditable data with per-field confidence estimation. It was introduced in the paper [Uncertainty-Aware Structured Data Extraction from Full CMR Reports via Distilled LLMs](https://huggingface.co/papers/2605.08045).
+
+## Overview
+
+The model is designed to support cohort assembly, longitudinal data curation, and clinical decision support in real-world clinical workflows. It performs structured information extraction from reports and assigns confidence scores to each extracted field, enabling efficient human review and quality control.
+
+---
+
+## Key Features
+
+- **Structured Extraction**: Converts free-text CMR reports into predefined structured fields  
+- **Per-field Confidence**: Provides uncertainty estimates for each extracted variable  
+- **Offline Inference**: Fully deployable without external API dependencies  
+- **Efficient Design**: Lightweight student model distilled from a larger teacher model  
+
+---
+
+## Code
+
+The official implementation is available on GitHub:  
+[CMR-EXTR](https://github.com/yuyi1005/CMR-EXTR)
+
+---
+
+## Method Summary
+
+CMR-EXTR is built on a teacher–student distillation framework:
+
+- A large teacher model generates high-quality structured outputs  
+- A compact student model (based on Llama-3.2-1B) is trained to replicate these outputs efficiently  
+- The student model supports fast and fully offline inference  
+
+Uncertainty estimation integrates three complementary principles:
+
+1. **Distribution Plausibility** — evaluates whether predictions follow expected value ranges  
+2. **Sampling Stability** — measures consistency under stochastic decoding  
+3. **Cross-field Consistency** — enforces logical relationships across extracted variables
+
+---
+
+## Citation
+
+If you use this work, please cite:
+
+```bibtex
+@inproceedings{yu2026uncertainty,
+  title={Uncertainty-Aware Structured Data Extraction from Full CMR Reports via Distilled LLMs},
+  author={Yu, Yi and Martin, Parker and Bu, Zhenyu and Liu, Yixuan and Zheng, Yi-Yu and Simonetti, Orlando and Han, Yuchi and Xue, Yuan},
+  booktitle={IEEE 23rd International Symposium on Biomedical Imaging (ISBI)},
+  year={2026},
+}
+```