python_requires=">=3.8",
install_requires=[
"torch>=2.1.0",
"transformers>=4.39.0",
"appdirs",
"jsonpickle",
"filelock",
"h5py",
"nltk",
"dotmap",
"pytest",
],
Testing:
pytestUsage:
from radgraph import RadGraph
radgraph = RadGraph(model_type="modern-radgraph-xl")
annotations = radgraph(["No evidence of pneumothorax following chest tube removal."])Output:
{
"0": {
"text": "No evidence of pneumothorax following chest tube removal .",
"entities": {
"1": {
"tokens": "pneumothorax",
"label": "Observation::definitely absent",
"start_ix": 3,
"end_ix": 3,
"relations": []
},
"2": {
"tokens": "chest",
"label": "Anatomy::definitely present",
"start_ix": 5,
"end_ix": 5,
"relations": []
},
"3": {
"tokens": "tube",
"label": "Observation::definitely present",
"start_ix": 6,
"end_ix": 6,
"relations": [
[
"located_at",
"2"
]
]
}
},
"data_source": null,
"data_split": "inference"
}
}
Official package as per:
@inproceedings{delbrouck-etal-2024-radgraph,
title = "{R}ad{G}raph-{XL}: A Large-Scale Expert-Annotated Dataset for Entity and Relation Extraction from Radiology Reports",
author = "Delbrouck, Jean-Benoit and
Chambon, Pierre and
Chen, Zhihong and
Varma, Maya and
Johnston, Andrew and
Blankemeier, Louis and
Van Veen, Dave and
Bui, Tan and
Truong, Steven and
Langlotz, Curtis",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.765",
pages = "12902--12915",
}Usage:
from radgraph import F1RadGraph
refs = ["no acute cardiopulmonary abnormality",
"endotracheal tube is present and bibasilar opacities likely represent mild atelectasis",
]
hyps = ["no acute cardiopulmonary abnormality",
"et tube terminates 2 cm above the carina and bibasilar opacities"
]
f1radgraph = F1RadGraph(reward_level="all", model_type="radgraph-xl")
mean_reward, reward_list, hypothesis_annotation_lists, reference_annotation_lists = f1radgraph(hyps=hyps, refs=refs)
rg_e, rg_er, rg_bar_er = mean_reward
print(mean_reward)Output:
(np.float64(0.75), np.float64(0.6666666666666666), np.float64(0.6538461538461539))
Over the years, RG_ER has been reported widely in RRG papers.
F1RadGraph as per:
@inproceedings{delbrouck-etal-2022-improving,
title = "Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards",
author = "Delbrouck, Jean-Benoit and
Chambon, Pierre and
Bluethgen, Christian and
Tsai, Emily and
Almusa, Omar and
Langlotz, Curtis",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.319",
pages = "4348--4360",
}import json
from radgraph import get_radgraph_processed_annotations, RadGraph
report = """
Mild pulmonary edema with probable small bilateral pleural effusions.
More focal opacities at lung bases may reflect atelectasis but infection cannot be completely excluded.
"""
model_type = "modern-radgraph-xl"
radgraph = RadGraph(model_type=model_type)
annotations = radgraph(
[report]
)
processed_annotations = get_radgraph_processed_annotations(annotations)
for annotation in processed_annotations["processed_annotations"]:
located_at = f" [Location: {', '.join(annotation['located_at'])}]" if annotation["located_at"] else ""
suggestive_of = f" [Suggestive of: {', '.join(annotation['suggestive_of'])}]" if annotation["suggestive_of"] else ""
tag = f" [Tag: {annotation['tags'][0]}]"
print(f"Observation: {annotation['observation']}{located_at}{suggestive_of}{tag}")Output:
Observation: mild edema [Location: pulmonary] [Tag: definitely present]
Observation: small effusions [Location: bilateral pleural] [Tag: uncertain]
Observation: focal opacities [Location: lung bases] [Suggestive of: opacities suggestive of atelectasis, opacities suggestive of infection] [Tag: definitely present]
Observation: atelectasis [Tag: uncertain]
Observation: infection [Tag: uncertain]
- Annotated MIMIC reports: https://physionet.org/content/radgraph-xl/1.0.0/ (300 samples)
- Annotated Stanford reports: https://stanfordaimi.azurewebsites.net/datasets/57816fe0-661c-4a95-8724-3f95b29568d9 (2000 samples)
- Annotated MIMIC-CXR reports: https://physionet.org/content/radgraph/1.0.0/ (600 samples)
radgraph = RadGraph(model="radgraph")
@inproceedings{delbrouck-etal-2024-radgraph,
title = "{R}ad{G}raph-{XL}: A Large-Scale Expert-Annotated Dataset for Entity and Relation Extraction from Radiology Reports",
author = "Delbrouck, Jean-Benoit and
Chambon, Pierre and
Chen, Zhihong and
Varma, Maya and
Johnston, Andrew and
Blankemeier, Louis and
Van Veen, Dave and
Bui, Tan and
Truong, Steven and
Langlotz, Curtis",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.765",
pages = "12902--12915",
}@inproceedings{delbrouck-etal-2022-improving,
title = "Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards",
author = "Delbrouck, Jean-Benoit and
Chambon, Pierre and
Bluethgen, Christian and
Tsai, Emily and
Almusa, Omar and
Langlotz, Curtis",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.319",
pages = "4348--4360",
}@inproceedings{NEURIPS DATASETS AND BENCHMARKS2021_c8ffe9a5,
author = {Jain, Saahil and Agrawal, Ashwin and Saporta, Adriel and Truong, Steven and Duong, Du Nguyen Duong Nguyen and Bui, Tan and Chambon, Pierre and Zhang, Yuhao and Lungren, Matthew and Ng, Andrew and Langlotz, Curtis and Rajpurkar, Pranav and Rajpurkar, Pranav},
booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks},
editor = {J. Vanschoren and S. Yeung},
pages = {},
publisher = {Curran},
title = {RadGraph: Extracting Clinical Entities and Relations from Radiology Reports},
url = {https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/c8ffe9a587b126f152ed3d89a146b445-Paper-round1.pdf},
volume = {1},
year = {2021}
}
