@inproceedings{wang-etal-2024-unity,
title = "Unity in Diversity: Collaborative Pre-training Across Multimodal Medical Sources",
author = "Wang, Xiaochen and
Luo, Junyu and
Wang, Jiaqi and
Zhong, Yuan and
Zhang, Xiaokun and
Wang, Yaqing and
Bhatia, Parminder and
Xiao, Cao and
Ma, Fenglong",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.199",
pages = "3644--3656",
abstract = "Although pre-training has become a prevalent approach for addressing various biomedical tasks, the current efficacy of pre-trained models is hindered by their reliance on a limited scope of medical sources. This limitation results in data scarcity during pre-training and restricts the range of applicable downstream tasks. In response to these challenges, we develop MedCSP, a new pre-training strategy designed to bridge the gap between multimodal medical sources. MedCSP employs modality-level aggregation to unify patient data within individual sources. Additionally, leveraging temporal information and diagnosis history, MedCSP effectively captures explicit and implicit correlations between patients across different sources. To evaluate the proposed strategy, we conduct comprehensive experiments, where the experiments are based on 6 modalities from 2 real-world medical data sources, and MedCSP is evaluated on 4 tasks against 19 baselines, marking an initial yet essential step towards cross-source modeling in the medical domain.",
}
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<abstract>Although pre-training has become a prevalent approach for addressing various biomedical tasks, the current efficacy of pre-trained models is hindered by their reliance on a limited scope of medical sources. This limitation results in data scarcity during pre-training and restricts the range of applicable downstream tasks. In response to these challenges, we develop MedCSP, a new pre-training strategy designed to bridge the gap between multimodal medical sources. MedCSP employs modality-level aggregation to unify patient data within individual sources. Additionally, leveraging temporal information and diagnosis history, MedCSP effectively captures explicit and implicit correlations between patients across different sources. To evaluate the proposed strategy, we conduct comprehensive experiments, where the experiments are based on 6 modalities from 2 real-world medical data sources, and MedCSP is evaluated on 4 tasks against 19 baselines, marking an initial yet essential step towards cross-source modeling in the medical domain.</abstract>
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%0 Conference Proceedings
%T Unity in Diversity: Collaborative Pre-training Across Multimodal Medical Sources
%A Wang, Xiaochen
%A Luo, Junyu
%A Wang, Jiaqi
%A Zhong, Yuan
%A Zhang, Xiaokun
%A Wang, Yaqing
%A Bhatia, Parminder
%A Xiao, Cao
%A Ma, Fenglong
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F wang-etal-2024-unity
%X Although pre-training has become a prevalent approach for addressing various biomedical tasks, the current efficacy of pre-trained models is hindered by their reliance on a limited scope of medical sources. This limitation results in data scarcity during pre-training and restricts the range of applicable downstream tasks. In response to these challenges, we develop MedCSP, a new pre-training strategy designed to bridge the gap between multimodal medical sources. MedCSP employs modality-level aggregation to unify patient data within individual sources. Additionally, leveraging temporal information and diagnosis history, MedCSP effectively captures explicit and implicit correlations between patients across different sources. To evaluate the proposed strategy, we conduct comprehensive experiments, where the experiments are based on 6 modalities from 2 real-world medical data sources, and MedCSP is evaluated on 4 tasks against 19 baselines, marking an initial yet essential step towards cross-source modeling in the medical domain.
%U https://aclanthology.org/2024.acl-long.199
%P 3644-3656
Markdown (Informal)
[Unity in Diversity: Collaborative Pre-training Across Multimodal Medical Sources](https://aclanthology.org/2024.acl-long.199) (Wang et al., ACL 2024)
ACL
- Xiaochen Wang, Junyu Luo, Jiaqi Wang, Yuan Zhong, Xiaokun Zhang, Yaqing Wang, Parminder Bhatia, Cao Xiao, and Fenglong Ma. 2024. Unity in Diversity: Collaborative Pre-training Across Multimodal Medical Sources. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3644–3656, Bangkok, Thailand. Association for Computational Linguistics.