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Related Experiment Video

Updated: Feb 19, 2026

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
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FiR-Rad: Fine-Grained Reinforcement With Structured Reasoning for Chest X-Ray Report Generation.

Xin Mei, Libin Yang, Dehong Gao

    IEEE Transactions on Medical Imaging
    |February 17, 2026
    PubMed
    Summary

    FiR-Rad enhances automated radiology reports using a two-stage approach for accurate and interpretable diagnostic reasoning. This framework improves clinical accuracy and transparency in chest X-ray analysis.

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    Area of Science:

    • Artificial Intelligence
    • Medical Imaging
    • Radiology

    Background:

    • Automated chest X-ray report generation needs clinical accuracy and interpretable reasoning.
    • Current methods often lack transparency in diagnostic processes.

    Purpose of the Study:

    • To propose FiR-Rad, a novel two-stage framework for automated chest X-ray report generation.
    • To enhance both the clinical accuracy and interpretability of AI-driven diagnostic reasoning.

    Main Methods:

    • A supervised chain-of-thought approach for sequential analysis of thoracic abnormalities.
    • Segment-level reinforcement learning with Group Relative Policy Optimization (GRPO) for fine-grained feedback.
    • Combining structured reasoning with targeted optimization for improved report synthesis.

    Main Results:

    • Achieved state-of-the-art performance on MIMIC-CXR and IU-Xray datasets.
    • Demonstrated strong zero-shot generalization capabilities on the IU-Xray dataset.
    • Significantly improved interpretability and clinical accuracy in generated reports.

    Conclusions:

    • FiR-Rad effectively addresses limitations in automated radiology report generation.
    • The framework provides transparent and interpretable diagnostic reasoning.
    • The proposed method enhances consistency between detailed analysis and final conclusions.