| 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} | |
| } | |
| ``` |