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.- Anthology ID:
- 2023.bionlp-1.56
- Volume:
- The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Dina Demner-fushman, Sophia Ananiadou, Kevin Cohen
- Venue:
- BioNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 545–549
- Language:
- URL:
- https://aclanthology.org/2023.bionlp-1.56
- DOI:
- 10.18653/v1/2023.bionlp-1.56
- Cite (ACL):
- Aaron Nicolson, Jason Dowling, and Bevan Koopman. 2023. e-Health CSIRO at RadSum23: Adapting a Chest X-Ray Report Generator to Multimodal Radiology Report Summarisation. In The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pages 545–549, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- e-Health CSIRO at RadSum23: Adapting a Chest X-Ray Report Generator to Multimodal Radiology Report Summarisation (Nicolson et al., BioNLP 2023)
- PDF:
- https://preview.aclanthology.org/fix-volume-bibkeys/2023.bionlp-1.56.pdf