damo-radar README

The project's README from github.com/alibaba-damo-academy/damo-radar. As one markdown file: README.md.

RADAR: An Expert-Level Generalist AI for Abdominal CT Diagnosis

Paper GitHub Zenodo Hugging Face ModelScope License

RADAR is a generalist vision-language model trained on over 400,000 contrast-enhanced abdominal CT examinations with 15 million anatomy-aware image–text pairs, learning directly from clinical reports without manual annotation. RADAR provides a scalable and versatile framework for radiology AI, demonstrating expert-level performance across both routine and complex clinical tasks.

RADAR Overview

Setup

Create a conda environment and install the required dependencies:

conda create -n radar python=3.10
conda activate radar
pip install -r requirements.txt

Model & Data

cd download_scripts
# Download model checkpoints and support files into ckpt/
python download_checkpoints.py
# Download auxiliary data (processed masks)
python download_auxiliary_data.py

Zenodo

Code can also be archived on Zenodo.

Documentation

For detailed instructions, please refer to the following guides:

Guide Description
Training Train RADAR/RADAR+ from scratch or fine-tune on MERLIN data; inference and evaluation are also included.
Inference 1. An inference demo with a radar pre-trained checkpoint on RAD-CT, and 2. Inference and evaluation of radar performance on the external MERLIN test set.
Preprocess Image/mask and radiology report preprocessing code, which can be used to process the MERLIN data or your own custom data.

Acknowledgements

This project is built upon the following open-source projects:

License

This project is released under the CC BY-NC-SA 4.0.

Portions of the code are derived from third-party open-source projects that are distributed under their own licenses (see the Acknowledgements above). Their original license texts are retained in THIRD_PARTY_LICENSES.md.

The Radar model is currently intended for research purposes only. Further improvements and prospective clinical studies are still required before it can be used directly for clinical deployment.

Citation

If you find RADAR useful in your research, please cite our paper:

@article{damo-radar-2026,
    author = {Qi Zhang and Jianpeng Zhang and Weiwei Cao and Zilin Lu and Wanxing Chang and Haonan Ding and Cao Chen and Zhi Li and Xing Xue and Sinuo Wang and Shaoteng Zhang and Yutong Xie and Yong Xia and Qi Wu and Zhongyi Shui and Xi Li and Zhilin Zheng and Yanjie Zhou and Tony C.W. Mok and Yingda Xia and Hongkan Wang and Xianghua Ye and Tao Ma and Jie Peng and Xiaoguang Wang and Jian Ding and Yuming Gao and Huazhen Ye and Yiping Liu and Dongjie Chen and Zhaomin Ni and Jianwen Ning and Wei Zhang and Jian Liu and Chaohui Yu and Shenghong Ju and Jianfeng Zhang and Wenbo Xiao and Ling Zhang and Tingbo Liang },
    title = {An expert-level generalist AI for abdominal CT diagnosis},
    journal = {Science},
    volume = {393},
    number = {6817},
    pages = {eaec6129},
    year = {2026},
    doi = {10.1126/science.aec6129},
    URL = {https://www.science.org/doi/abs/10.1126/science.aec6129}
}

damo-radar (alibaba-damo-academy/damo-radar)

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208 ·GitHub
Damo-radar
这是什么？RADAR 是一个面向腹部 CT 诊断的通用视觉-语言 AI 模型，训练数据包含超过 40 万例增强腹部 CT 检查，以及直接从临床报告提取的 1500 万个具有解剖意识的图文对。
这是开箱即用的诊断工具，还是需要我自己训练/微调的研究模型？它是一个研究级的预训练视觉-语言模型，附带训练、微调和推理代码；预训练检查点托管在 HuggingFace 和 ModelScope 上。
怎么让它跑起来？创建 conda 环境并安装所需依赖，然后从 HuggingFace 下载预训练检查点和配套文件（downloadscripts/ 下提供了辅助脚本），再按照 Training/Inference/Preprocess 指南操作。
它依赖哪些数据和工具？需要一个带 PyTorch/CUDA 的 conda 环境，以及来自 HuggingFace 的预训练检查点和演示 NIfTI 文件；代码基于 LAVIS、nnU-Net、MONAI 和 3D-ResNets-PyTorch 构建。
能用它做真实的临床诊断吗？不能，作者明确表示它仅供研究使用，在进一步验证之前还不能用于临床部署。
这个许可证允许商业使用吗？不允许，它采用 CC BY-NC-SA 4.0 许可证，禁止商用且要求相同方式共享；捆绑的第三方组件保留各自独立的许可证。
可以用什么测试数据来评估它？文档包含使用预训练检查点在 RAD-CT 数据集上做推理和评估的指南，以及在外部 MERLIN 测试集上评估的指南。
能用我自己的 CT 影像做训练吗？可以，Preprocess 指南涵盖影像/掩膜和放射报告的预处理，你可以据此处理 MERLIN 数据或自己的自定义数据用于训练。
RADAR 与典型的分类器模型有什么不同？它直接从原始临床放射报告端到端训练，无需人工标注，在 1500 万个图文对上学习具有解剖意识的图文对齐，而不是依赖人工标注的类别。
What are similar projects?LongCat-Next, Qwen-Drive-1.0, ZDTaichu5.0-9B, OraRL, sam2, pointdit, EasyWAM, vegaml
Which topics is it in?vision-language-models, medical-imaging, multimodal-ai, healthcare, vision, foundation-models, vision-language-model, pretrained-models, fine-tuning, multimodal, transformer, academic-research
Creator
alibaba-damo-academy
Size
511 MB
Stars
208
Updated
CommitSep 22
Age
3mo
Commits · 90 Days