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CGFTrans: Cross-Modal Global Feature Fusion Transformer for Medical Report Generation.

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    This study introduces a new method for generating medical reports from images, improving accuracy and efficiency. The cross-modal global feature fusion Transformer (CGFTrans) enhances global information extraction and reduces computational load.

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    Area of Science:

    • Artificial Intelligence
    • Medical Informatics
    • Computer Vision

    Background:

    • Medical report generation from images is crucial for clinical applications.
    • Existing methods struggle with global information, cross-modal fusion, and computational demands.

    Purpose of the Study:

    • To propose a novel cross-modal global feature fusion Transformer (CGFTrans) for improved medical report generation.
    • To address limitations in global information extraction, cross-modal fusion, and computational efficiency.

    Main Methods:

    • Utilized a mesh recurrent network to capture inter-layer information for global features.
    • Designed a feature fusion decoder with a 'mid-fusion' strategy for enhanced cross-modal learning.
    • Integrated shifted window attention into the Transformer encoder to reduce computational pressure and capture multi-scale pathological information.

    Main Results:

    • Achieved average increments of 2.9% (BLEU-1), 1.5% (METEOR), and 0.7% (ROUGE-L) on three datasets.
    • Reduced training time by 22.4% and increased image throughput by 17.3%.

    Conclusions:

    • CGFTrans effectively extracts global information and enhances cross-modal fusion for medical report generation.
    • The proposed method significantly improves report generation quality while reducing computational strain.