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    <abstract>Radiology report generation (RRG) requires advanced medical image analysis, effective temporal reasoning, and accurate text generation. While multimodal large language models (MLLMs) align with pre-trained vision encoders to enhance visual-language understanding, most existing methods rely on single-image analysis or rule-based heuristics to process multiple images, failing to fully leverage temporal information in multi-modal medical datasets. In this paper, we introduce **Libra**, a temporal-aware MLLM tailored for chest X-ray report generation. Libra combines a radiology-specific image encoder with a novel Temporal Alignment Connector (**TAC**), designed to accurately capture and integrate temporal differences between paired current and prior images. Extensive experiments on the MIMIC-CXR dataset demonstrate that Libra establishes a new state-of-the-art benchmark among similarly scaled MLLMs, setting new standards in both clinical relevance and lexical accuracy. All source code and data are publicly available at: https://github.com/X-iZhang/Libra.</abstract>
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%0 Conference Proceedings
%T Libra: Leveraging Temporal Images for Biomedical Radiology Analysis
%A Zhang, Xi
%A Meng, Zaiqiao
%A Lever, Jake
%A Ho, Edmond S. L.
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F zhang-etal-2025-libra
%X Radiology report generation (RRG) requires advanced medical image analysis, effective temporal reasoning, and accurate text generation. While multimodal large language models (MLLMs) align with pre-trained vision encoders to enhance visual-language understanding, most existing methods rely on single-image analysis or rule-based heuristics to process multiple images, failing to fully leverage temporal information in multi-modal medical datasets. In this paper, we introduce **Libra**, a temporal-aware MLLM tailored for chest X-ray report generation. Libra combines a radiology-specific image encoder with a novel Temporal Alignment Connector (**TAC**), designed to accurately capture and integrate temporal differences between paired current and prior images. Extensive experiments on the MIMIC-CXR dataset demonstrate that Libra establishes a new state-of-the-art benchmark among similarly scaled MLLMs, setting new standards in both clinical relevance and lexical accuracy. All source code and data are publicly available at: https://github.com/X-iZhang/Libra.
%R 10.18653/v1/2025.findings-acl.888
%U https://aclanthology.org/2025.findings-acl.888/
%U https://doi.org/10.18653/v1/2025.findings-acl.888
%P 17275-17303

Markdown (Informal)

[Libra: Leveraging Temporal Images for Biomedical Radiology Analysis](https://aclanthology.org/2025.findings-acl.888/) (Zhang et al., Findings 2025)

• Libra: Leveraging Temporal Images for Biomedical Radiology Analysis (Zhang et al., Findings 2025)

ACL

• Xi Zhang, Zaiqiao Meng, Jake Lever, and Edmond S. L. Ho. 2025. Libra: Leveraging Temporal Images for Biomedical Radiology Analysis. In Findings of the Association for Computational Linguistics: ACL 2025, pages 17275–17303, Vienna, Austria. Association for Computational Linguistics.