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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Activating Associative Disease-Aware Vision Token Memory for LLM-Based X-Ray Report Generation.

Xiao Wang, Fuling Wang, Haowen Wang

    IEEE Transactions on Medical Imaging
    |August 27, 2025
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    This study introduces an enhanced AI model for generating X-ray reports, improving disease description by integrating visual information and historical data. The new model mimics doctors' reasoning for more accurate medical imaging reports.

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    Area of Science:

    • Artificial Intelligence
    • Medical Imaging Analysis
    • Natural Language Processing

    Background:

    • Large language models (LLMs) have advanced X-ray report generation but often overlook crucial visual details.
    • Existing models produce linguistically sound reports that may lack sufficient clinical disease information.
    • Effective integration of visual data and historical context is needed for superior medical report generation.

    Purpose of the Study:

    • To develop an associative memory-enhanced model for X-ray report generation.
    • To improve the descriptive accuracy of medical reports by better utilizing visual information.
    • To mimic the comprehensive report writing process of professional radiologists.

    Main Methods:

    • Utilized a classification model with activation maps to identify disease-related visual regions and learn disease query tokens.
    • Employed a visual Hopfield network for memory association of disease tokens.
    • Integrated a report Hopfield network to retrieve historical report information for enhanced generation.
    • Leveraged a large language model for final report synthesis.

    Main Results:

    • Achieved state-of-the-art performance on benchmark datasets: IU X-ray, MIMIC-CXR, and Chexpert Plus.
    • Demonstrated improved ability to describe key diseases compared to previous methods.
    • Generated high-quality medical reports by effectively combining visual and textual information.

    Conclusions:

    • The proposed associative memory-enhanced model significantly improves X-ray report generation.
    • Integrating global and local visual information with historical report data enhances clinical accuracy.
    • The model offers a promising approach for more comprehensive and accurate AI-driven medical reporting.