@inproceedings{lovelace-mortazavi-2020-learning,
title = "Learning to Generate Clinically Coherent Chest {X}-Ray Reports",
author = "Lovelace, Justin and
Mortazavi, Bobak",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.110",
doi = "10.18653/v1/2020.findings-emnlp.110",
pages = "1235--1243",
abstract = "Automated radiology report generation has the potential to reduce the time clinicians spend manually reviewing radiographs and streamline clinical care. However, past work has shown that typical abstractive methods tend to produce fluent, but clinically incorrect radiology reports. In this work, we develop a radiology report generation model utilizing the transformer architecture that produces superior reports as measured by both standard language generation and clinical coherence metrics compared to competitive baselines. We then develop a method to differentiably extract clinical information from generated reports and utilize this differentiability to fine-tune our model to produce more clinically coherent reports.",
}
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<abstract>Automated radiology report generation has the potential to reduce the time clinicians spend manually reviewing radiographs and streamline clinical care. However, past work has shown that typical abstractive methods tend to produce fluent, but clinically incorrect radiology reports. In this work, we develop a radiology report generation model utilizing the transformer architecture that produces superior reports as measured by both standard language generation and clinical coherence metrics compared to competitive baselines. We then develop a method to differentiably extract clinical information from generated reports and utilize this differentiability to fine-tune our model to produce more clinically coherent reports.</abstract>
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%0 Conference Proceedings
%T Learning to Generate Clinically Coherent Chest X-Ray Reports
%A Lovelace, Justin
%A Mortazavi, Bobak
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F lovelace-mortazavi-2020-learning
%X Automated radiology report generation has the potential to reduce the time clinicians spend manually reviewing radiographs and streamline clinical care. However, past work has shown that typical abstractive methods tend to produce fluent, but clinically incorrect radiology reports. In this work, we develop a radiology report generation model utilizing the transformer architecture that produces superior reports as measured by both standard language generation and clinical coherence metrics compared to competitive baselines. We then develop a method to differentiably extract clinical information from generated reports and utilize this differentiability to fine-tune our model to produce more clinically coherent reports.
%R 10.18653/v1/2020.findings-emnlp.110
%U https://aclanthology.org/2020.findings-emnlp.110
%U https://doi.org/10.18653/v1/2020.findings-emnlp.110
%P 1235-1243
Markdown (Informal)
[Learning to Generate Clinically Coherent Chest X-Ray Reports](https://aclanthology.org/2020.findings-emnlp.110) (Lovelace & Mortazavi, Findings 2020)
ACL