@inproceedings{loch-etal-2024-ihealth,
title = "i{H}ealth-{C}hile-3{\&}2 at {RRG}24: Template Based Report Generation",
author = "Loch, Oscar and
Messina, Pablo and
Elberg, Rafael and
Campanini, Diego and
Soto, {\'A}lvaro and
Vidal, Ren{\'e} and
Parra, Denis",
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Miwa, Makoto and
Roberts, Kirk and
Tsujii, Junichi",
booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.bionlp-1.53",
pages = "614--623",
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.",
}
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%0 Conference Proceedings
%T iHealth-Chile-3&2 at RRG24: Template Based Report Generation
%A Loch, Oscar
%A Messina, Pablo
%A Elberg, Rafael
%A Campanini, Diego
%A Soto, Álvaro
%A Vidal, René
%A Parra, Denis
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Miwa, Makoto
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F loch-etal-2024-ihealth
%X 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.
%U https://aclanthology.org/2024.bionlp-1.53
%P 614-623
Markdown (Informal)
[iHealth-Chile-3&2 at RRG24: Template Based Report Generation](https://aclanthology.org/2024.bionlp-1.53) (Loch et al., BioNLP-WS 2024)
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.