---
license: apache-2.0
---
<div align="center">
<h1>UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation</h1>

<a href="https://arxiv.org/abs/2504.21336"><img src='https://img.shields.io/badge/arXiv-Preprint-red' alt='Paper PDF'></a>
<a href='https://huggingface.co/Luffy503/UniBiomed'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue'></a>
<a href='https://huggingface.co/datasets/Luffy503/UniBiomed'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-green' alt='Dataset'></a>
</div>

We introduce **UniBiomed**, the first universal foundation model for grounded biomedical image interpretation, which is capable of generating accurate diagnostic findings and simultaneously segmenting the corresponding biomedical targets. UniBiomed is based on a novel integration of Multi-modal Large Language Model (MLLM) and Segment Anything Model (SAM), which can effectively unify diverse biomedical tasks in universal training for advancing grounded interpretation.

Github link: https://github.com/Luffy03/UniBiomed

We will consistently update more powerful versions of models in this repo.

## Usage
```python
import argparse
import torch
from transformers import (AutoModel, AutoTokenizer,
                          BitsAndBytesConfig, CLIPImageProcessor,
                          GenerationConfig)
def parse_args():
    parser = argparse.ArgumentParser(description='UniBiomed')
    parser.add_argument('--model_path', default='Luffy503/UniBiomed')
    return args
args = parse_args()

# load model
model = AutoModel.from_pretrained(
        args.model_path,
        torch_dtype=torch.bfloat16,
        low_cpu_mem_usage=True,
        use_flash_attn=True,
        trust_remote_code=True,
    ).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(
    args.model_path,
    trust_remote_code=True,
)

# define data input, image and text instruction
data_dict = {}
image, text = None, None
data_dict['image'] = image
data_dict['text'] = text

# output
pred_dict = model.predict_forward(**data_dict, tokenizer=tokenizer)
# text description
prediction = pred_dict['prediction']
# segmentation mask
mask = pred_dict['prediction_masks'][0][0]
```

## Citation

If you find this repo useful for your research, please consider citing the paper as follows:

```bibtex
@article{wu2025unibiomed,
  title={UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation},
  author={Wu, Linshan and Nie, Yuxiang and He, Sunan and Zhuang, Jiaxin and Chen, Hao},
  journal={arXiv preprint arXiv:2504.21336},
  year={2025}
}
```