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The official start-up code for paper "FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark."

FFA-IR 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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# FFA-IR
The official start-up code for paper "FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark."
 
The framework is inherited from [R2Gen](https://github.com/cuhksz-nlp/R2Gen).

## Data

NEWS: Our latest version, including all FFA images and annotation files, is available on [PhysioNet](https://physionet.org/content/ffa-ir-medical-report/1.1.0/).

To extract all the files, please first download all the files in FFAIR, and use the command "cat FAIR.tar.gz.* | 
tar -zxv". Then the name of each directory refers to the case ID, and all the FFA images are provided.

Please put the data and annotation files in 'code/data' directory. Or you can change the code in main.py to fit your own condition.

## Requirements
- torch==1.5.1
- torchvision==0.6.1
- opencv-python==4.4.0.42

## Training

You can directly run our code by the following:

```python
python main.py
```

## Contact

If you are interested in this dataset or have any questions, please connect us: Mingjie.Li@monash.edu.

**The lesion_dict.json is missing on the Physionet, we are working with them to update a new version. Before that anyone with the approval license from Physionet can email Mingjie.li@monash.edu to access the data.**

If you find the dataset or code useful, please cite our paper:

~~~
@inproceedings{li2021ffa,
  title={FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark},
  author={Li, Mingjie and Cai, Wenjia and Liu, Rui and Weng, Yuetian and Zhao, Xiaoyun and Wang, Cong and Chen, Xin and Liu, Zhong and Pan, Caineng and Li, Mengke and others},
  booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
  year={2021}
}
~~~

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