This is the implementation of the CDGPT2 model mentioned in our paper 'Automated Radiology Report Generation using Conditioned Transformers'.

Paper link here.

We automatically generate full radiology reports given chest X-ray images from the IU-X-Ray dataset by conditioning a pre-trained GPT2 model on the visual and semantic features of the image.

Model checkpoint here.

The project was tested on a virtual environment of python 3.7, pip 23.2.1, and MacOS

• pip install -r full_requirements.txt (or pip install -r requirements.txt if there are errors because of using a different operating system, as requirements.txt only contains the main dependencies and pip will fetch the compatible sub-dependencies, but it will be slower)
• nlg-eval --setup
• python get_iu_xray.py (to download the dataset)
• python train.py

• VSGRU repository here.
• Finetuned Chexnet repository here.

To cite this paper, please use:

@article{ALFARGHALY2021100557,
title = {Automated radiology report generation using conditioned transformers},
journal = {Informatics in Medicine Unlocked},
volume = {24},
pages = {100557},
year = {2021},
issn = {2352-9148},
doi = {https://doi.org/10.1016/j.imu.2021.100557},
url = {https://www.sciencedirect.com/science/article/pii/S2352914821000472},
author = {Omar Alfarghaly and Rana Khaled and Abeer Elkorany and Maha Helal and Aly Fahmy}
}