iHealth-Chile-3&2 at RRG24: Template Based Report Generation
Oscar Loch, Pablo Messina, Rafael Elberg, Diego Campanini, Álvaro Soto, René Vidal, Denis Parra
Abstract
This paper presents the approaches of the iHealth-Chile-3 and iHealth-Chile-2 teams for the shared task of Large-Scale Radiology Report Generation at the BioNLP workshop. Inspired by prior work on template-based report generation, both teams focused on exploring various template-based strategies, using predictions from multi-label image classifiers as input. Our best approach achieved a modest F1-RadGraph score of 19.42 on the findings hidden test set, ranking 7th on the leaderboard. Notably, we consistently observed a discrepancy between our classification metrics and the F1-CheXbert metric reported on the leaderboard, which always showed lower scores. This suggests that the F1-CheXbert metric may be missing some of the labels mentioned by the templates.- Anthology ID:
- 2024.bionlp-1.53
- Volume:
- Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Dina Demner-Fushman, Sophia Ananiadou, Makoto Miwa, Kirk Roberts, Junichi Tsujii
- Venues:
- BioNLP | WS
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 614–623
- Language:
- URL:
- https://aclanthology.org/2024.bionlp-1.53
- DOI:
- 10.18653/v1/2024.bionlp-1.53
- Cite (ACL):
- Oscar Loch, Pablo Messina, Rafael Elberg, Diego Campanini, Álvaro Soto, René Vidal, and Denis Parra. 2024. iHealth-Chile-3&2 at RRG24: Template Based Report Generation. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 614–623, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- iHealth-Chile-3&2 at RRG24: Template Based Report Generation (Loch et al., BioNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2024.bionlp-1.53.pdf