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Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays.

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    Summary
    This summary is machine-generated.

    Radiomics-Guided Transformer (RGT) accurately localizes and classifies cardiopulmonary pathologies in chest X-rays using only image-level labels. This novel approach integrates radiomic features without requiring bounding box annotations, improving diagnostic accuracy.

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    Area of Science:

    • Medical Image Analysis
    • Artificial Intelligence in Healthcare
    • Radiomics and Deep Learning

    Background:

    • Traditional medical image analysis relied on handcrafted radiomic features, demanding precise pathology localization.
    • Accurate localization is challenging in real-world settings, limiting the effectiveness of previous automated methods.
    • Existing deep learning models for chest X-rays often overlook domain-specific radiomic features.

    Purpose of the Study:

    • To propose a novel Radiomics-Guided Transformer (RGT) for accurate cardiopulmonary pathology localization and classification.
    • To develop a method that integrates global image context with local radiomic information without requiring bounding box annotations.
    • To leverage image-level disease labels for end-to-end pathology localization and classification.

    Main Methods:

    • Developed a Radiomics-Guided Transformer (RGT) model comprising an image Transformer branch and a radiomics Transformer branch.
    • Implemented fusion layers and cross-attention mechanisms to integrate image and radiomic features.
    • Utilized a self-attention mechanism to extract bounding boxes for radiomic feature computation, creating a feedback loop for localization.

    Main Results:

    • RGT achieved superior performance in weakly supervised disease localization on the NIH ChestXRay dataset, outperforming prior methods by an average of 3.6% across IoU thresholds.
    • Demonstrated improved classification accuracy, achieving 1.1% higher average area under the receiver operating characteristic curve.
    • Successfully localized pathologies and classified diseases using only image-level labels, validating the end-to-end feedback loop.

    Conclusions:

    • The Radiomics-Guided Transformer (RGT) offers a powerful, annotation-efficient approach for medical image analysis.
    • Integrating radiomic features guided by Transformer attention significantly enhances pathology localization and classification accuracy.
    • The proposed method sets a new benchmark for weakly supervised learning in chest X-ray analysis.