@inproceedings{nicolson-etal-2023-e,
title = "e-Health {CSIRO} at {R}ad{S}um23: Adapting a Chest {X}-Ray Report Generator to Multimodal Radiology Report Summarisation",
author = "Nicolson, Aaron and
Dowling, Jason and
Koopman, Bevan",
editor = "Demner-fushman, Dina and
Ananiadou, Sophia and
Cohen, Kevin",
booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.bionlp-1.56",
doi = "10.18653/v1/2023.bionlp-1.56",
pages = "545--549",
abstract = "We describe the participation of team e-Health CSIRO in the BioNLP RadSum task of 2023. This task aims to develop automatic summarisation methods for radiology. The subtask that we participated in was multimodal; the impression section of a report was to be summarised from a given findings section and set of Chest X-rays (CXRs) of a subject{'}s study. For our method, we adapted an encoder-to-decoder model for CXR report generation to the subtask. e-Health CSIRO placed seventh amongst the participating teams with a RadGraph ER F1 score of 23.9.",
}
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%0 Conference Proceedings
%T e-Health CSIRO at RadSum23: Adapting a Chest X-Ray Report Generator to Multimodal Radiology Report Summarisation
%A Nicolson, Aaron
%A Dowling, Jason
%A Koopman, Bevan
%Y Demner-fushman, Dina
%Y Ananiadou, Sophia
%Y Cohen, Kevin
%S The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F nicolson-etal-2023-e
%X We describe the participation of team e-Health CSIRO in the BioNLP RadSum task of 2023. This task aims to develop automatic summarisation methods for radiology. The subtask that we participated in was multimodal; the impression section of a report was to be summarised from a given findings section and set of Chest X-rays (CXRs) of a subject’s study. For our method, we adapted an encoder-to-decoder model for CXR report generation to the subtask. e-Health CSIRO placed seventh amongst the participating teams with a RadGraph ER F1 score of 23.9.
%R 10.18653/v1/2023.bionlp-1.56
%U https://aclanthology.org/2023.bionlp-1.56
%U https://doi.org/10.18653/v1/2023.bionlp-1.56
%P 545-549
Markdown (Informal)
[e-Health CSIRO at RadSum23: Adapting a Chest X-Ray Report Generator to Multimodal Radiology Report Summarisation](https://aclanthology.org/2023.bionlp-1.56) (Nicolson et al., BioNLP 2023)
ACL