PPD 5a63bf7044 | 1 year ago | |
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data | 1 year ago | |
models | 1 year ago | |
modules | 1 year ago | |
pycocoevalcap | 1 year ago | |
README.md | 1 year ago | |
annotation.json | 1 year ago | |
log.txt | 1 year ago | |
mainForCXR.py | 1 year ago | |
mainForIU.py | 1 year ago | |
requirements.txt | 1 year ago | |
run_iu_xray.sh | 1 year ago | |
run_mimic_cxr.sh | 1 year ago |
This is the implementation of Generating Radiology Reports via Memory-driven Transformer at EMNLP-2020.
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",
}
torch==1.5.1
torchvision==0.6.1
opencv-python==4.4.0.42
You can download the models we trained for each dataset from here.
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 bash run_iu_xray.sh
to train a model on the IU X-Ray data.
Run bash run_mimic_cxr.sh
to train a model on the MIMIC-CXR data.
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