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A Python package for evaluating Radiology Report Generation (RRG) using multiple metrics including:
BLEU, ROUGE, METEOR, BERTScore, F1RadGraph, F1CheXbert, and SembScore.

• Multiple evaluation metrics supported:
  • BLEU
  • ROUGE
  • METEOR
  • BERTScore
  • F1 RadGraph
  • F1 CheXbert
  • SembScore (CheXbert vector similarity)
  • RaTEScore (Entity-aware metric)
• Easy-to-use API
• Support for batch processing
• Detailed per-sample and aggregated results
• Visualization tools for correlation analysis

• Add CLI usage
• AddGREEN score

1. Clone the repository:

git clone https://github.com/jogihood/rrg-metric.gitcd rrg-metric

2. Create and activate a conda environment using the providedenvironment.yml:

conda env create -f environment.ymlconda activate rrg-metric

Alternatively, you can install the required packages using pip:

pip install -r requirements.txt

Here's a simple example of how to use the package:

importrrg_metric# Example usagepredictions= ["Normal chest x-ray","Bilateral pleural effusions noted"]ground_truth= ["Normal chest radiograph","Small bilateral pleural effusions present"]# Compute BLEU scoreresults=rrg_metric.compute(metric="bleu",preds=predictions,gts=ground_truth,per_sample=True,verbose=True)print(f"Total BLEU score:{results['total_results']}")ifresults['per_sample_results']:print(f"Per-sample scores:{results['per_sample_results']}")

The package provides visualization tools for correlation analysis between metric scores and radiologist error counts:

For preprocessing tools related to radiology error validation (ReXVal), please check:https://github.com/jogihood/rexval-preprocessor

importrrg_metricimportmatplotlib.pyplotasplt# Example datametric_scores= [0.8,0.7,0.9,0.6,0.85]error_counts= [1,2,0,3,1]# Create correlation plotax,tau,tau_ci=rrg_metric.plot_corr(metric="BLEU",metric_scores=metric_scores,radiologist_error_counts=error_counts,error_type="total",# or "significant"color='blue',# custom colorscatter_alpha=0.6,# scatter point transparencyshow_tau=True# show Kendall's tau in title)print(f"Kendall's tau:{tau:.3f}")print(f"95% CI: [{tau_ci[0]:.3f},{tau_ci[1]:.3f}]")plt.show()

• metric (str): The evaluation metric to use. Must be one of: ["bleu", "rouge", "meteor", "bertscore", "f1radgraph", "chexbert", "ratescore"]
• preds (List[str]): List of model predictions/generated texts
• gts (List[str]): List of ground truth/reference texts

• per_sample (bool, default=False): If True, returns scores for each individual prediction-reference pair
• verbose (bool, default=False): If True, displays progress bars and loading messages
• f1radgraph_model_type /f1radgraph_reward_level: Parameters for RadGraph. Recommend default values
• cache_dir:cache_dir for huggingface model downloads

• metric (str): Name of the metric being visualized
• metric_scores (List[float]): List of metric scores
• radiologist_error_counts (List[float]): List of radiologist error counts

• error_type (str, default="total"): Type of error to plot. Must be either "total" or "significant"
• ax (matplotlib.axes.Axes, default=None): Matplotlib axes for plotting. If None, creates new figure and axes
• Additional parameters for plot customization (see docstring for details)

• Python 3.10+
• Other dependencies listed inrequirements.txt

This repository is still under active development. If you encounter any issues or bugs, I would really appreciate if you could submit a Pull Request. Your contributions will help make this package more robust and useful for the community!