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    This study introduces an automated method to detect mislabeled data in deep learning models, crucial for accurate classification. Correcting these labels significantly improves model performance in clinical applications.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Medical Imaging

    Background:

    • Data quality is paramount in deep learning, directly impacting classification performance.
    • Weakly-supervised learning models struggle with potentially inaccurate or mislabeled training data.
    • Accurate data labeling is critical for reliable deep learning model development.

    Purpose of the Study:

    • To propose an automated framework for identifying mislabeled data in deep learning.
    • To evaluate the impact of correcting mislabeled data on classification performance.
    • To assess the effectiveness of the proposed method in real-world clinical scenarios.

    Main Methods:

    • Developed a framework using Cross-entropy Loss and Influence functions to detect mislabeled data.
    • Applied the method to 10,500 images for breast density and malignancy classification.
    • Evaluated performance by introducing intentionally flipped labels at varying proportions.

    Main Results:

    • The method identified up to 98% of mislabeled data in datasets with 10% noise.
    • Correcting identified mislabels led to improved classification performance.
    • Outperformed two published schemes in breast density classification experiments.

    Conclusions:

    • The proposed automated framework effectively identifies mislabeled data in deep learning.
    • Correcting mislabels enhances classification accuracy, particularly in medical imaging.
    • This method offers a practical solution for handling inaccurate labels in weakly-supervised learning.