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# RadFM
The official code for "Towards Generalist Foundation Model for Radiology"

[ArXiv](http://arxiv.org/abs/2308.02463)
<img src="https://github.com/chaoyi-wu/RadFM/blob/main/Images/GIF.gif"/>

[ArXiv](http://arxiv.org/abs/2308.02463)

[Website](https://chaoyi-wu.github.io/RadFM/)

[Model checkpoint](https://huggingface.co/chaoyi-wu/RadFM)

[Website](https://chaoyi-wu.github.io/RadFM/)

<img src="https://github.com/chaoyi-wu/RadFM/blob/main/Images/GIF.gif"/>

## Quick Start:
Download [Model checkpoint](https://huggingface.co/chaoyi-wu/RadFM) and check `./src/test.py` for how to generate text with our model.
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* Check the [data_csv](https://huggingface.co/datasets/chaoyi-wu/RadFM_data_csv) (uploading) to get how different datasets are processed and down load them into `./src/Dataset/data_csv`
* Modify the path as you disire, and check ```./src/train.py``` to pre-train.

## Case Study:


<img src="https://github.com/chaoyi-wu/RadFM/blob/main/Images/result_vqa.jpg"/>
<img src="https://github.com/chaoyi-wu/RadFM/blob/main/Images/result_report_generation.jpg"/>
<img src="https://github.com/chaoyi-wu/RadFM/blob/main/Images/result_report.jpg"/>
<img src="https://github.com/chaoyi-wu/RadFM/blob/main/Images/result_rationale.jpg"/>

## To-do List:
- Polish the code for easier usage.
- Update an easy sample for a quick start.
Expand Down Expand Up @@ -58,6 +54,15 @@ MedKD Dataset downloading URL:
| 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 |

## Case Study:

Some cases produced by our final model:

<img src="https://github.com/chaoyi-wu/RadFM/blob/main/Images/result_vqa.jpg"/>
<img src="https://github.com/chaoyi-wu/RadFM/blob/main/Images/result_report.jpg"/>
<img src="https://github.com/chaoyi-wu/RadFM/blob/main/Images/result_rationale.jpg"/>


## 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.

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