scuq-rrg 1.0.2

Semantic Consistency-Based Uncertainty Quantification for Radiology Report Generation

SCUQ-RRG

Code for the NAACL 2025 paper "Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation".

Install

pip install scuq-rrg

For full functionality (GREEN model and RadGraph):

git clone --recurse-submodules https://github.com/Heimerd1nger/SCUQ-RRG.git
cd SCUQ-RRG
pip install -e .
pip install -e third_party/GREEN/   # green_score (PyPI version has incompatible API)
pip install radgraph                # sentence-level UQ

Usage

Report-Level Uncertainty (VRO-GREEN)

Measures report-level factual uncertainty by comparing a greedy-decoded report against multiple sampled reports using the GREEN metric.

from scuq import ReportUncertaintyScorer

scorer = ReportUncertaintyScorer(
    model_id_or_path="StanfordAIMI/GREEN-radllama2-7b",
    cuda=True,
)

# greedy_report: the reference (greedy-decoded) report
# sampled_reports: list of 10 stochastically sampled reports
greedy_report = "The lungs are clear. No pleural effusion. Cardiomediastinal silhouette is normal."
sampled_reports = [
    "Lungs are clear bilaterally. No effusion or pneumothorax.",
    "Clear lungs. Heart size normal. No acute findings.",
    # ... (typically 10 samples)
]

result = scorer.score(greedy_report, sampled_reports)
print(f"Uncertainty: {result.uncertainty:.3f}")   # e.g. 0.596
print(f"Mean GREEN:  {result.mean_green:.3f}")    # e.g. 0.404

Sentence-Level Uncertainty (VRO-RadGraph)

Identifies the most uncertain sentence in a report using RadGraph entity consistency.

from scuq import SentenceUncertaintyScorer

scorer = SentenceUncertaintyScorer()

greedy_report = (
    "No pneumothorax. "
    "Possible left lower lobe opacity suggesting pneumonia. "
    "Mild cardiomegaly. "
    "No pleural effusion. "
    "Stable appearance compared to prior. "
    "No acute osseous abnormality."
)
sampled_reports = [
    "No pneumothorax or effusion. Heart size normal.",
    "Bilateral lungs clear. No acute findings.",
    # ...
]

result = scorer.score(greedy_report, sampled_reports)
# Per-sentence uncertainty scores (0 = certain, 1 = uncertain):
# [0.05, 0.60, 0.80, 0.40, 0.28, 0.10]
print(f"Most uncertain: '{result.flagged_sentence}'")
print(f"Sentence scores: {[round(s, 2) for s in result.uncertainty_scores]}")

Data Format

Experiments expect:

• greedy_reports: list of N strings (greedy-decoded reports)
• sampled_reports: list of N lists, each with 10 sampled strings

Citation

@inproceedings{wang2025semantic,
  title={Semantic consistency-based uncertainty quantification for factuality in radiology report generation},
  author={Wang, Chenyu and Zhou, Weichao and Ghosh, Shantanu and Batmanghelich, Kayhan and Li, Wenchao},
  booktitle={Findings of the Association for Computational Linguistics: NAACL 2025},
  pages={1739--1754},
  year={2025}
}