Libra: Leveraging Temporal Images for Biomedical Radiology Analysis (2025)

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.

## introduction radiology reports are critical for biomedical radiology analysis, offering structured summaries of imaging studies such as chest x-rays (cxrs). commonly divided into sections like findings, impression, indication, technique, comparison, and history @xcite , these reports guide diagnostic and therapeutic decisions @xcite . however, manually generating such reports is both complex and time-consuming. automating radiology report generation (rrg) holds great promise for alleviating radiologist burnout, increasing efficiency, and improving communication @xcite . despite this, the intricate nature of medical imaging demands precise and detailed documentation, making rrg a challenging task. ## ablation studies we conducted ablation studies on libra's key components, evaluating module and dataset configurations. all experiments were performed on the mimic-cxr test split for the findings generation, with prior images included by default and consistent hyperparameters during training and inference. ## performance analysis we qualitatively assess libra's ability to generate temporally consistent radiology reports. cases without prior image as shown in figure 3 (a) , libra produced detailed descriptions beyond the ground truth, identifying "sternal wires" and their type. this demonstrates its capability to deliver clinically relevant information without spurious referencing nonexistent prior studies. ## conclusion in this study, we introduced libra, a temporalaware multimodal large language model tailored for chest x-ray report generation tasks. libra employs a two-stage training framework, leveraging a radiology-specific image encoder and language model connected via the temporal alignment connector, enabling seamless integration of visual and textual modalities. trained solely on the open-access mimic-cxr dataset @xcite , libra demonstrates notable performance gains across key metrics compared to similarly scaled models. through qualitative and quantitative analysis, we showed that libra effectively utilises temporal relationships between current and prior scans, addressing challenges such as hallucinations in referencing prior studies. this highlights libra's ability to generate clinically accurate and temporally consistent radiology reports, setting a new paradigm for multimodal medical ai research. future work will focus on expanding libra's clinical applicability by incorporating diverse imaging modalities and enhancing temporal reasoning capabilities, and extending it in an agentic way.