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Classifying Biomedical Figures by Modality via Multi-Label Learning.

Athanasios Lagopoulos, Nikolaos Kapraras, Vasileios Amanatiadis

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    This study introduces new methods for classifying biomedical figures by modality, improving search and retrieval. These approaches enhance information access for researchers and clinicians using visual and textual data.

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    Area of Science:

    • Biomedical Informatics
    • Medical Image Analysis
    • Information Retrieval

    Background:

    • Biomedical figures are crucial for research, education, and clinical decisions.
    • Lack of metadata and diverse figure types hinder effective search and retrieval.
    • Current methods often require complex figure segmentation.

    Purpose of the Study:

    • To develop novel multi-label modality classification approaches for biomedical figures.
    • To investigate the effectiveness of classifying figures with and without prior compound figure detection.
    • To evaluate the impact of multimodal learning on classification and compound figure detection.

    Main Methods:

    • Developed multi-label classification models using visual features from simple and compound biomedical figures.
    • Compared performance with and without separating compound figures into sub-figures.
    • Integrated visual and textual features for multimodal learning.

    Main Results:

    • Proposed multi-label classification approaches effectively categorize biomedical figures by modality.
    • Classifying all figures or only those predicted as compound yielded comparable results.
    • Multimodal learning improved both modality classification and compound figure detection.

    Conclusions:

    • Novel multi-label classification methods enhance biomedical figure retrieval without segmentation.
    • Multimodal learning offers significant benefits for biomedical figure analysis.
    • A web application facilitates accessible search and feedback for biomedical figures.