Awesome

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.

Data

Our dataset, including all FFA images and annotation files, is available on PhysioNet.

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 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}
}