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Feature Imitating Networks Enhance the Performance, Reliability and Speed of Deep Learning on Biomedical Image

Shangyang Min, Hassan B Ebadian, Tuka Alhanai

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
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    Summary

    Feature-Imitating-Networks (FINs) enhance biomedical image processing. Models with embedded FINs showed improved performance and faster convergence in tasks like COVID-19 detection and brain tumor analysis.

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    Area of Science:

    • Artificial Intelligence
    • Medical Imaging
    • Machine Learning

    Background:

    • Feature-Imitating-Networks (FINs) are a novel neural network architecture.
    • FINs are initially trained to approximate statistical features, then integrated into other networks.
    • Their application in biomedical image processing has not been previously evaluated.

    Purpose of the Study:

    • To conduct the first evaluation of FINs for biomedical image processing tasks.
    • To assess the impact of embedding FINs on network performance and convergence.
    • To compare FIN-embedded networks against baseline models with similar or greater capacity.

    Main Methods:

    • Trained FINs to imitate six common radiomics features.
    • Embedded trained FINs into larger neural networks.
    • Evaluated performance on three tasks: COVID-19 detection (CT), brain tumor classification (MRI), and brain tumor segmentation (MRI).

    Main Results:

    • FIN-embedded models outperformed baseline networks across all three tasks.
    • Performance enhancement was observed even when baseline networks had more parameters.
    • FIN-embedded models demonstrated faster and more consistent convergence compared to baselines.

    Conclusions:

    • FINs significantly enhance performance in biomedical image processing tasks.
    • FINs offer a promising approach for improving diagnostic accuracy and efficiency in medical imaging.
    • The findings suggest FINs could achieve state-of-the-art results in various medical image analysis applications.