RadFM

The official code for the paper "Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data"

ArXiv

Website

Model checkpoint

In this project, we collect a large-scale medical multi-modal dataset, MedMD, with 16M 2D or 3D images. We train a new medical multi-modal generative model RadFM on it, enabling both 2D and 3D scans, multi-image input and visual-language interleaving cases.

Latest News：

All Datasets are released! We have updated the links in our dataset table. You can find all our text part data in https://huggingface.co/datasets/chaoyi-wu/RadFM_data_csv.

We also update the PMC-Figure links which contains all PMC-series datasets' figures, you can download it once and all dataset will use the figures.

For decompressing the splited compression files in most cases, please check the following code in linux:

cat zip.z* > myzip.zip
unzip myzip.zip

Quick Start:

For quick start, you can check the Quick_demo path.
We demonstrate a simple diagnosis case here to show how to inference with our model.
Feel free to modify it as you want.

• S1. Download Model checkpoint or form baiduyun (No need for decompressing).

• S2. Decompress the original zip file, you can get a pytorch_model.bin.

• S3. put pytorch_model.bin under path Quick_demo/.

• S4. python test.py and you can get a conversation as:

  Input: Can you identify any visible signs of Cardiomegaly in the image?
  Output: yes

By the way, never try to perform this in cpu and gpu is all you need :）.

Pre-train:

For re-training a model on our dataset or large-scale testing our pre-train model, you can check src.

Simply, train.py for training and test.py for testing.

• Check the data_csv to get how different datasets are processed and download them into src/Dataset/data_csv
• Modify the path as you disire, and check src/train.py to pre-train or src/train.py to test.

Case Study:

Some cases produced by our final model:

Dataset-Links:

MedKD Dataset downloading URL:

Dataset Name	Link	Access
Rad3D-series	https://pan.baidu.com/s/1E_uSoCLm5H66a7KkpRfi1g?pwd=urfg or https://onedrive.live.com/?id=FCA8CA4C877919CB!23996&resid=FCA8CA4C877919CB!23996&ithint=folder&authkey=!AN6taBTpTQ16xqA&cid=fca8ca4c877919cb	Open Access
MPx-series	https://pan.baidu.com/s/1tSn6OibIoMZLddagFoMRdw?pwd=mhxb or https://huggingface.co/datasets/chaoyi-wu/MedPix-Images/viewer/default/train?p=1	Open Access
PMC-Figures	https://pan.baidu.com/s/1Src_rhXsaOFp8zJ_3zMFsQ?pwd=p3ne	Open Access
PMC-Inline	https://huggingface.co/datasets/chaoyi-wu/PMC-Inline	Open Access
PMC-CaseReport	Original version, Filtered version	Open Access
VinDr-Mammo	https://www.physionet.org/content/vindr-mammo/1.0.0/	Credentialed Access
VinDr-SpineXR	https://www.physionet.org/content/vindr-spinexr/1.0.0/	Credentialed Access
VinDr-PCXR	https://physionet.org/content/vindr-pcxr/1.0.0/	Credentialed Access
PMC-OA	https://huggingface.co/datasets/axiong/pmc_oa_beta	Open Access
PMC-VQA	https://huggingface.co/datasets/xmcmic/PMC-VQA	Open Access
VQA-RAD	https://osf.io/89kps/	Open Access
SLAKE	https://www.med-vqa.com/slake/	Open Access
MIMIC-CXR	https://physionet.org/content/mimic-cxr/2.0.0	Credentialed Access
VinDr-CXR	https://physionet.org/content/vindr-cxr/1.0.0/	Credentialed Access
NIH ChestXray14	https://nihcc.app.box.com/v/ChestXray-NIHCC/folder/36938765345	Open Access
CheXpert	https://aimi.stanford.edu/chexpert-chest-x-rays	Open Access
Covid-CXR2	https://www.kaggle.com/datasets/andyczhao/covidx-cxr2	Open Access
NLM-TB	Montgomery, ChinaSet	Open Access
Object-CXR	https://web.archive.org/web/20201127235812/https://jfhealthcare.github.io/object-CXR/	Open Access
OpenI	https://www.kaggle.com/datasets/raddar/chest-xrays-indiana-university	Open Access
RSNA	https://www.rsna.org/education/ai-resources-and-training/ai-image-challenge/rsna-pneumonia-detection-challenge-2018	Open Access
SIIM-ACR	https://www.kaggle.com/datasets/jesperdramsch/siim-acr-pneumothorax-segmentation-data	Open Access

Acknowledgment:

We sincerely thank all the contributors who uploaded the relevant data in our dataset online. We appreciate their willingness to make these valuable cases publicly available.

Contact

If you have any questions, please feel free to contact wtzxxxwcy02@sjtu.edu.cn.