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Transfer Representation Learning using Inception-V3 for the Detection of Masses in Mammography.

Y Mednikov, S Nehemia, B Zheng

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |November 17, 2018
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    Summary

    This study introduces a computer-aided diagnosis (CAD) scheme for mammography using Inception-V3 deep learning. The AI model shows promise in improving breast cancer detection accuracy from mammograms.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Oncology

    Background:

    • Breast cancer is the most common cancer in women.
    • Mammography is the primary screening tool, but interpretation is challenging and time-consuming.
    • Skilled interpretation is crucial for accurate breast cancer diagnosis.

    Purpose of the Study:

    • To develop a computer-aided diagnosis (CAD) scheme for mammography interpretation.
    • To leverage transfer representation learning with the Inception-V3 architecture for breast cancer detection.
    • To address the challenge of limited dataset size in medical imaging AI.

    Main Methods:

    • Utilized the Inception-V3 architecture for feature extraction from mammograms.
    • Employed transfer representation learning to enhance model performance.
    • Expanded the training dataset using artificial mammogram generation and data augmentation techniques.
    • Evaluated the CAD scheme on the INBreast database.

    Main Results:

    • The proposed CAD scheme demonstrated significant potential in mammography analysis.
    • Achieved a maximal area under the receiver operating characteristics curve (AUC) of 0.91.
    • Feature extraction from various layers of the Inception-V3 architecture was explored.

    Conclusions:

    • The developed AI-based CAD scheme shows promise for assisting radiologists in breast cancer detection.
    • Transfer learning and data augmentation are effective strategies for overcoming small dataset limitations in medical AI.
    • The Inception-V3 architecture provides a robust foundation for developing advanced mammography analysis tools.