@inproceedings{munkhoeva-etal-2024-airi,
title = "{AIRI} at {RRG}24: {LL}a{V}a with specialised encoder and decoder",
author = "Munkhoeva, Marina and
Umerenkov, Dmitry and
Samokhin, Valentin",
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-1.51",
pages = "603--607",
abstract = "We present a new approach to generating the {`}Findings{'} and {`}Impression{'} sections in the chest X-rays radiology reports, developed as part of the shared radiology task at BioNLP 2024. By integrating a DINOv2 vision encoder trained on medical data with specialized biomedical large language model using the LLaVA framework, our method addresses complex medical semantics and diverse findings in imaging. We use datasets from PadChest, BIMCV-COVID19, CheXpert, OpenI, and MIMIC-CXR. The evaluation metrics demonstrate our method{'}s effectiveness and the potential for automating the generation of radiology reports.",
}
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<abstract>We present a new approach to generating the ‘Findings’ and ‘Impression’ sections in the chest X-rays radiology reports, developed as part of the shared radiology task at BioNLP 2024. By integrating a DINOv2 vision encoder trained on medical data with specialized biomedical large language model using the LLaVA framework, our method addresses complex medical semantics and diverse findings in imaging. We use datasets from PadChest, BIMCV-COVID19, CheXpert, OpenI, and MIMIC-CXR. The evaluation metrics demonstrate our method’s effectiveness and the potential for automating the generation of radiology reports.</abstract>
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%0 Conference Proceedings
%T AIRI at RRG24: LLaVa with specialised encoder and decoder
%A Munkhoeva, Marina
%A Umerenkov, Dmitry
%A Samokhin, Valentin
%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 munkhoeva-etal-2024-airi
%X We present a new approach to generating the ‘Findings’ and ‘Impression’ sections in the chest X-rays radiology reports, developed as part of the shared radiology task at BioNLP 2024. By integrating a DINOv2 vision encoder trained on medical data with specialized biomedical large language model using the LLaVA framework, our method addresses complex medical semantics and diverse findings in imaging. We use datasets from PadChest, BIMCV-COVID19, CheXpert, OpenI, and MIMIC-CXR. The evaluation metrics demonstrate our method’s effectiveness and the potential for automating the generation of radiology reports.
%U https://aclanthology.org/2024.bionlp-1.51
%P 603-607
Markdown (Informal)
[AIRI at RRG24: LLaVa with specialised encoder and decoder](https://aclanthology.org/2024.bionlp-1.51) (Munkhoeva et al., BioNLP-WS 2024)
ACL