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Related Experiment Video

Updated: May 24, 2025

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Adaptive Metadata-Guided Supervised Contrastive Learning for Domain Adaptation on Respiratory Sound Classification.

June-Woo Kim, Miika Toikkanen, Amin Jalali

    IEEE Journal of Biomedical and Health Informatics
    |March 3, 2025
    PubMed
    Summary

    This study enhances respiratory sound classification (RSC) models by using metadata to reduce bias from recording inconsistencies and demographic imbalances. The new methods significantly improve model accuracy and reduce domain dependency for real-world applications.

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    Area of Science:

    • Medical Informatics
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Deep learning models for respiratory sound classification (RSC) face challenges due to biased datasets from inconsistent recording and imbalanced demographics.
    • This bias leads to poor performance when models are deployed in real-world scenarios.

    Purpose of the Study:

    • To address bias in RSC datasets by exploring metadata-guided domain adaptation techniques.
    • To improve the generalizability and accuracy of RSC models across different data sources.

    Main Methods:

    • Investigated the impact of various metadata attributes and their combinations on RSC model performance.
    • Developed an advanced method for adaptive rescaling of metadata combinations to enhance domain adaptation during training.

    Main Results:

    • Demonstrated a significant reduction in domain dependency for RSC models.
    • Achieved an improved detection accuracy score of 84.97%, a 7.37% enhancement over the baseline model.
    • Validated the effectiveness of the proposed methods on both the ICBHI dataset and a custom dataset.

    Conclusions:

    • Metadata-guided domain adaptation is effective in mitigating bias and improving RSC model performance.
    • The proposed adaptive rescaling method offers a substantial advancement for training robust and accurate respiratory sound classification models.