e-Health CSIRO at RadSum23: Adapting a Chest X-Ray Report Generator to Multimodal Radiology Report Summarisation
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Dowling, J
Koopman, B
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Toronto, Canada
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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The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
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© 2023 Association for Computational Linguistics. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Health policy
Biomedical imaging
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Nicolson, A; Dowling, J; Koopman, B, e-Health CSIRO at RadSum23: Adapting a Chest X-Ray Report Generator to Multimodal Radiology Report Summarisation, The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, 2023, pp. 545-549