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RIHA: Report-Image Hierarchical Alignment for Radiology Report Generation.

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    A new Report-Image Hierarchical Alignment Transformer (RIHA) improves radiology report generation by aligning images with reports at multiple levels. This hierarchical approach enhances accuracy in automatically generating diagnostic reports from medical images.

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    Area of Science:

    • Artificial Intelligence
    • Medical Imaging
    • Natural Language Processing

    Background:

    • Radiology report generation (RRG) aims to automate diagnostic report creation from medical images, reducing radiologist workload and errors.
    • Current RRG methods often fail to capture the hierarchical structure of reports, treating them as flat sequences, which limits alignment accuracy.
    • Achieving fine-grained alignment between complex visual features and the semantic hierarchy of radiology reports remains a significant challenge.

    Purpose of the Study:

    • To propose a novel end-to-end framework, RIHA (Report-Image Hierarchical Alignment Transformer), for precise multi-level alignment between radiological images and reports.
    • To enhance the accuracy and clinical efficacy of automated radiology report generation by addressing the limitations of existing methods.
    • To improve cross-modal mapping for capturing nuanced semantics in clinical narratives.

    Main Methods:

    • RIHA employs a hierarchical alignment strategy across paragraph, sentence, and word levels.
    • Introduces a Visual Feature Pyramid (VFP) for multi-scale visual feature extraction and a Text Feature Pyramid (TFP) for multi-granularity textual structure representation.
    • Utilizes a Cross-modal Hierarchical Alignment (CHA) module with optimal transport for feature alignment and Relative Positional Encoding (RPE) in the decoder for enhanced token-level alignment.

    Main Results:

    • RIHA demonstrates superior performance compared to state-of-the-art models on benchmark chest X-ray datasets (IU-Xray and MIMIC-CXR).
    • The proposed hierarchical alignment significantly improves natural language generation metrics for radiology reports.
    • The framework shows enhanced clinical efficacy in generating accurate and contextually relevant diagnostic reports.

    Conclusions:

    • RIHA offers a significant advancement in radiology report generation by effectively addressing the challenge of hierarchical cross-modal alignment.
    • The multi-level alignment strategy enables more precise mapping of visual information to textual descriptions, leading to improved report quality.
    • This hierarchical approach holds promise for more reliable and accurate automated diagnostic reporting in clinical practice.