libra-v1.0-3b

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Libra Model Card

Version: Libra-v1.0

Overview

A Small Multimodal Language Model for Radiology Report Generation, following the same training strategy as Libra. It can produce detailed Findings sections with temporal comparisons (e.g., comparing a current chest X-ray with prior ones). Libra integrates the following key components:

• RAD-DINO: A vision encoder pre-trained on medical imaging datasets for robust feature extraction from chest X-rays.
• Llama-3.2-3B-Instruct: A 3B-parameter language model (based on Llama-3.2). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks.
• Temporal Alignment Connector (TAC): A custom adapter that fuses features across multiple time points to enable temporal comparisons.

This model card provides an overview of Libra’s architecture, training methodology, limitations, and recommended usage guidelines.

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Paper and Resources

For more detailed information regarding Libra’s methodology, theoretical foundation, and performance benchmarks, please refer to the following resources:

• Project Website: Libra v1.0
• Paper: arXiv:2411.19378
• Code Repository: X-iZhang/Libra (GitHub)

Or check out our Spaces demo!

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Training Strategy

Libra is trained in a two-stage process:

1. Temporal Feature Alignment

   • Trains TAC to effectively fuse and align features from different time points (current and previous chest X-rays).
   • Focuses on capturing notable changes (e.g., appearance or progression of opacities, devices, and lines).

2. Fine-Tuning for Radiology Report Generation

   • The language model part is fine-tuned on a large dataset of paired chest X-ray images and radiology reports.
   • Emphasizes the generation of the Findings section, especially incorporating temporal descriptors.

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Intended Use

Libra is primarily designed to assist clinical practitioners, researchers, and medical students in generating chest X-ray reports. Key applications include:

• Clinical Decision Support: Providing draft findings that can be refined by a radiologist.
• Educational Tool: Demonstrating example interpretations and temporal changes for training radiology residents.
• Research: Facilitating studies on automated report generation and temporal feature learning in medical imaging.

Important: Outputs should be reviewed by qualified radiologists or medical professionals before final clinical decisions are made.

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Limitations and Recommendations

1. Data Bias: The model’s performance may be less reliable for underrepresented demographics or rare pathologies.
2. Clinical Oversight: Always involve a medical professional to verify the results—Libra is not a substitute for professional judgment.
3. Temporal Inaccuracies: Despite TAC’s focus on temporal alignment, subtle or uncommon changes may go unrecognized.
4. Generalization: Libra’s performance on chest X-ray types or conditions not seen during training may be limited.

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Ethical Considerations

• Patient Privacy: Ensure the data is fully de-identified and compliant with HIPAA/GDPR (or relevant privacy regulations).
• Responsible Use: Deploy Libra’s outputs carefully; they are not guaranteed to be error-free.
• Accountability: Users and organizations must assume responsibility for verifying clinical accuracy and safety.

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How to Cite ✒️

If you use Libra in academic or research contexts, please cite:

@inproceedings{zhang-etal-2025-libra,
    title = "Libra: Leveraging Temporal Images for Biomedical Radiology Analysis",
    author = "Zhang, Xi  and
      Meng, Zaiqiao  and
      Lever, Jake  and
      Ho, Edmond S. L.",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-acl.888/",
    pages = "17275--17303",
    ISBN = "979-8-89176-256-5",
    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."
}

@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}
}

Disclaimer:

This tool is for research and educational purposes only. It is not FDA-approved or CE-marked for clinical use. Users should consult qualified healthcare professionals for any clinical decisions.