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README.md

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-license: cc-by-4.0
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+---
+license: cc-by-4.0
+---
+
+<div align="center">
+<h1>
+  MedITok: A Unified Tokenizer for Medical Image Synthesis and Interpretation
+</h1>
+</div>
+
+
+<p align="center">
+📝 <a href="https://arxiv.org/abs/2505.19225" target="_blank">Paper</a> • 🤗 <a href="https://huggingface.co/massaki75/meditok/tree/main" target="_blank">Hugging Face</a> • 🧩 <a href="https://github.com/Masaaki-75/meditok" target="_blank">Github</a>
+</p>
+
+## 🚀 Introduction
+MedITok is the first unified visual tokenizer for medical images. Trained on 30M medical images and 2M image-caption pairs via a two-stage representation learning framework, MedITok:
+- effectively encodes visual details and clinical semantics into a unified token space
+- achieves state-of-the-art performance across diverse medical imaging modalities and tasks.
+- can be incorporated into prevelant generative models (e.g., autoregressive architectures) for downstream medical image synthesis and interpretation.
+
+
+
+## ✏️ Citation
+
+```
+@article{ma2025meditok,
+  title={MedITok: A Unified Tokenizer for Medical Image Synthesis and Interpretation},
+  author={Ma, Chenglong and Ji, Yuanfeng and Ye, Jin and Li, Zilong and Wang, Chenhui and Ning, Junzhi and Li, Wei and Liu, Lihao and Guo, Qiushan and Li, Tianbin and He, Junjun and Shan, Hongming},
+  journal={arXiv preprint arXiv:2505.19225},
+  year={2025}
+}
+
+```