---
pipeline_tag: image-classification
---

# CheXGenBench: Patient Re-Identification Network

This repository contains the Patient Re-Identification Network (RPN) based on a ResNet-50 architecture, as presented in the paper [CheXGenBench: A Unified Benchmark For Fidelity, Privacy and Utility of Synthetic Chest Radiographs](https://huggingface.co/papers/2505.10496).

The model is trained to identify if two chest radiographs (X-rays) belong to the same patient. Within the CheXGenBench framework, it serves as a key component for evaluating the privacy risks and clinical utility of synthetic chest radiograph generation by assessing whether generative models are memorizing or reproducing training data identities.

## Links

- **Paper:** [CheXGenBench: A Unified Benchmark For Fidelity, Privacy and Utility of Synthetic Chest Radiographs](https://huggingface.co/papers/2505.10496)
- **Project Page:** [https://raman1121.github.io/CheXGenBench/](https://raman1121.github.io/CheXGenBench/)
- **GitHub Repository:** [https://github.com/Raman1121/CheXGenBench](https://github.com/Raman1121/CheXGenBench)

## Usage

This model is intended to be used as part of the CheXGenBench evaluation suite to calculate privacy and patient re-identification metrics. For detailed instructions on environment setup and running evaluation scripts (such as `privacy_metrics.sh`), please refer to the [GitHub repository](https://github.com/Raman1121/CheXGenBench).

## Citation

```bibtex
@article{dutt2025chexgenbench,
  title={CheXGenBench: A Unified Benchmark For Fidelity, Privacy and Utility of Synthetic Chest Radiographs},
  author={Dutt, Raman and Sanchez, Pedro and Yao, Yongchen and McDonagh, Steven and Tsaftaris, Sotirios A and Hospedales, Timothy},
  journal={arXiv preprint arXiv:2505.10496},
  year={2025}
}
```