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Dataset from Shared task on Large-Scale Radiology Report Generation (https://stanford-aimi.github.io/RRG24/). Access requires a verified CITI training certificate using the same process outlined by PhysioNet (see https://physionet.org/about/citi-course/) Please provide proof via the verification URL, which takes the form https://www.citiprogram.org/verify/?XXXXXX. You agree to not use the model to conduct experiments that cause harm to human subjects.
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✏️ Citation
@inproceedings{xu-etal-2024-overview,
title = "Overview of the First Shared Task on Clinical Text Generation: {RRG}24 and {\textquotedblleft}Discharge Me!{\textquotedblright}",
author = "Xu, Justin and
Chen, Zhihong and
Johnston, Andrew and
Blankemeier, Louis and
Varma, Maya and
Hom, Jason and
Collins, William J. and
Modi, Ankit and
Lloyd, Robert and
Hopkins, Benjamin and
Langlotz, Curtis and
Delbrouck, Jean-Benoit",
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Miwa, Makoto and
Roberts, Kirk and
Tsujii, Junichi",
booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.bionlp-1.7/",
doi = "10.18653/v1/2024.bionlp-1.7",
pages = "85--98",
}
Interpret-CXR: A Large-scale Collection of CXR datasets.
📝 Paper • 🤗 Hugging Face • 🧩 Github • 🪄 Project
✨ Latest News
- [02/20/2024]: Shared task at BioNLP@ACL2024 online [Website].
💡 Motivation
We curated the "Interpret-CXR" dataset for the following motivations:
- For the shared task on large-scale radiology report generation at BioNLP@ACL2024.
- Simplify the data access process.
- Standardize the benchmark for future research in this field
🎬 Get Started
from datasets import load_dataset
dataset = load_dataset("StanfordAIMI/interpret-cxr-public")
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