Libra: Leveraging Temporal Images for Biomedical Radiology AnalysisRadiology 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.
Explore the capabilities of Libra with our interactive demo.
Temporal hallucination is a critical challenge in radiology report generation (RRG). Traditional multimodal large language models (MLLMs) struggle to integrate prior images correctly, often generating:
Libra addresses these limitations by integrating a Temporal Alignment Connector (TAC) to improve temporal awareness, ensuring:
We propose Libra (Leveraging Temporal Images for Biomedical Radiology Analysis), a novel framework tailored for radiology report generation (RRG) that incorporates temporal change information to address the challenges of interpreting medical images effectively.
Libra leverages RAD-DINO, a pre-trained visual transformer, as its image encoder to generate robust and scalable image features. These features are further refined by a Temporal Alignment Connector (TAC), a key innovation in Libra's architecture. The TAC comprises:
These refined features are fed into Meditron, a specialised medical large language model (LLM), to generate comprehensive, temporally-aware radiology reports. Libraβs modular design seamlessly integrates state-of-the-art open-source pre-trained models for both image and text, aligning them through a temporal-aware adapter to ensure robust cross-modal reasoning and understanding.
Through a two-stage training strategy, Libra demonstrates the powerful potential of multimodal large language models (MLLMs) in specialised radiology applications. Extensive experiments on the MIMIC-CXR dataset highlight Libra's performance, setting a new state-of-the-art benchmark among models of the same parameter scale.
@inproceedings{zhang2025libra,
title={Libra: Leveraging temporal images for biomedical radiology analysis},
author={Zhang, Xi and Meng, Zaiqiao and Lever, Jake and Ho, Edmond SL},
booktitle={Findings of the Association for Computational Linguistics: ACL 2025},
pages={17275--17303},
year={2025}
}
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