Add model card and metadata for CheXGenBench Re-ID model

README.md

@@ -1 +1,30 @@
-Patient Re-Identification Network trained to identify if two radiographs (X-rays) belong to the same patient.
+---
+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}
+}
+```