update readme

README.md

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 ---
 license: apache-2.0
+base_model:
+- ByteDance-Seed/BAGEL-7B-MoT
+pipeline_tag: any-to-any
+library_name: bagel-mot
+language:
+- en
+datasets:
+- Astrostellar/RadGenome-Brain_MRI_parquet
+tags:
+- medical
+- medical-imaging
+- brain-mri
+- multimodal
+- image-to-image
+- image-text-to-text
+- modality-imputation
 ---
+
+# UniBrain: Unified Multimodal Model for Brain MRI Imputation and Understanding
+
+<p align="left">
+  <a href="https://medicalumm.github.io/unibrain.github.io/"><img src="https://img.shields.io/badge/UniBrain-Project_Page-0A66C2?logo=safari&logoColor=white" alt="UniBrain project page"></a>
+  <a href="https://arxiv.org/abs/2606.16484"><img src="https://img.shields.io/badge/UniBrain-Paper-red?logo=arxiv&logoColor=white" alt="UniBrain paper"></a>
+  <a href="https://github.com/zhiyuns/UniBrain"><img src="https://img.shields.io/badge/UniBrain-Code-536af5?logo=github&logoColor=white" alt="UniBrain code"></a>
+</p>
+
+> **UniBrain** is a unified multimodal model for brain MRI analysis. In one autoregressive context, it can impute missing MRI sequences, interpret the available and generated images, and produce a disease diagnosis. This repository hosts the UniBrain model checkpoints.
+
+For installation, training, evaluation, and usage instructions, please visit the [official GitHub repository](https://github.com/zhiyuns/UniBrain).
+
+<p align="center">
+  <img src="https://github.com/zhiyuns/UniBrain/raw/main/assets/main_figure.png" alt="Overview of the UniBrain framework" width="95%">
+</p>
+
+UniBrain is initialized from [BAGEL-7B-MoT](https://huggingface.co/ByteDance-Seed/BAGEL-7B-MoT), a Mixture-of-Transformer-Experts (MoT) model for multimodal understanding and generation. It adapts BAGEL to brain MRI using an interleaved, description-enriched training flow and three main ideas:
+
+- **Unified MRI generation and understanding:** missing-sequence imputation and downstream interpretation share one autoregressive context.
+- **Self-alignment:** medical image reconstruction provides dense supervision for fine-grained anatomical representation learning without requiring detailed captions for every image.
+- **Dynamic hidden states:** training conditions the model on its own generated visual context to reduce exposure bias during long multimodal sequences.
+
+## Model details
+
+| Item | Description |
+| --- | --- |
+| Base model | [ByteDance-Seed/BAGEL-7B-MoT](https://huggingface.co/ByteDance-Seed/BAGEL-7B-MoT) |
+| Architecture | Unified MoT architecture |
+| Domain | 2D axial brain MRI slices |
+| Tasks | MRI modality imputation, brain MRI understanding/diagnosis |
+| Training data | [RadGenome-Brain_MRI](https://huggingface.co/datasets/JiayuLei/RadGenome-Brain_MRI), using the [UniBrain preprocessed release](https://huggingface.co/datasets/Astrostellar/RadGenome-Brain_MRI_parquet) |
+| Inference precision | BF16 |
+
+
+## Reported results
+
+The following results are reported on the RadGenome-Brain MRI evaluation split in the paper and [project page](https://medicalumm.github.io/unibrain.github.io/).
+
+### MRI diagnosis and report generation
+
+| Available modalities | Top-1 diagnosis accuracy (%) | ROUGE |
+| --- | ---: | ---: |
+| T1w only | 74.47 | 36.93 |
+| T1w + T2w | 76.60 | 38.23 |
+| T1w + T2w + T2-FLAIR | 78.01 | 38.68 |
+| Complete data | 82.06 | 38.94 |
+
+### MRI modality imputation
+
+| Imputation sequence | PSNR | Downstream Top-1 accuracy (%) |
+| --- | ---: | ---: |
+| T1w → T2w | 22.23 | 68.09 |
+| T1w, T2w → T2-FLAIR | 22.58 | 67.38 |
+| T1w, T2w, T2-FLAIR → T1c | 22.26 | 74.47 |
+
+
+## License
+
+The UniBrain model weights are released under the Apache License 2.0. UniBrain builds on [BAGEL](https://github.com/ByteDance-Seed/Bagel) and [AutoRG-Brain](https://github.com/ljy19970415/AutoRG-Brain); the code, base model, incorporated components, and datasets retain their respective licenses and terms.
+
+## Acknowledgements
+
+The implementation is adapted from [BAGEL](https://github.com/ByteDance-Seed/Bagel), a unified multimodal foundation model for natural images. The training and evaluation data are based on [RadGenome-Brain_MRI](https://huggingface.co/datasets/JiayuLei/RadGenome-Brain_MRI) from the [AutoRG-Brain](https://github.com/ljy19970415/AutoRG-Brain) project.
+
+## Citation
+
+If you find UniBrain useful, please cite:
+
+```bibtex
+@article{unibrain2026,
+  title   = {Unified Multimodal Model for Brain MRI Imputation and Understanding},
+  author  = {Zhiyun Song, Che Liu, Tian Xia, Avinash Kori, Wenjia Bai},
+  journal = {arXiv preprint arXiv:2606.16484},
+  year    = {2026}
+}
+```
+