R2GEN/pycocoevalcap

• R2Gen
• • Citations
  • Requirements
  • Download R2Gen
  • Datasets
  • Run on IU X-Ray
  • Run on MIMIC-CXR

R2Gen

This is the implementation of Generating Radiology Reports via Memory-driven Transformer at EMNLP-2020.

Citations

If you use or extend our work, please cite our paper at EMNLP-2020.

@inproceedings{chen-emnlp-2020-r2gen,
    title = "Generating Radiology Reports via Memory-driven Transformer",
    author = "Chen, Zhihong and
      Song, Yan  and
      Chang, Tsung-Hui and
      Wan, Xiang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2020",
}

Requirements

• torch==1.5.1
• torchvision==0.6.1
• opencv-python==4.4.0.42

Download R2Gen

You can download the models we trained for each dataset from here.

Datasets

We use two datasets (IU X-Ray and MIMIC-CXR) in our paper.

For IU X-Ray, you can download the dataset from here and then put the files in data/iu_xray.

For MIMIC-CXR, you can download the dataset from here and then put the files in data/mimic_cxr.

Run on IU X-Ray

Run bash run_iu_xray.sh to train a model on the IU X-Ray data.

Run on MIMIC-CXR

Run bash run_mimic_cxr.sh to train a model on the MIMIC-CXR data.