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Deep Neural Networks With Region-Based Pooling Structures for Mammographic Image Classification.

Xin Shu, Lei Zhang, Zizhou Wang

    IEEE Transactions on Medical Imaging
    |January 28, 2020
    PubMed
    Summary

    This study introduces novel deep neural network pooling structures for mammogram classification, reducing the need for manual data annotation. These methods improve breast cancer detection accuracy without requiring segmentation masks.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Oncology

    Background:

    • Mammography is crucial for breast cancer screening, but traditional AI methods require extensive manual annotation.
    • Manual labeling is costly, time-consuming, and limits the scalability of AI-driven diagnostic tools.
    • Deep neural networks offer potential but often need detailed ground truth data for training.

    Purpose of the Study:

    • To develop an end-to-end mammogram classification method that minimizes the need for manual data annotation.
    • To introduce novel pooling structures for Convolutional Neural Networks (CNNs) to enhance mammographic image analysis.
    • To improve the efficiency and reduce the cost of building AI classifiers for breast cancer detection.

    Main Methods:

    • Proposed an end-to-end full-image mammogram classification approach using deep neural networks.

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  • Developed specialized pooling structures for CNNs, focusing on high-probability malignancy regions instead of common methods.
  • The method requires only image-level classification labels, not bounding boxes or mask annotations.

    • Main Results:

    • The proposed pooling structures were applied to CNN-based models, improving performance on mammographic data.
    • Experimental results on the INbreast and CBIS datasets demonstrated satisfactory performance compared to state-of-the-art methods.
    • The approach achieved competitive results without relying on segmentation annotations.

    Conclusions:

    • The novel pooling structures offer a cost-effective and efficient alternative for AI-based mammogram classification.
    • This method reduces the reliance on manual segmentation, potentially accelerating the adoption of AI in breast cancer screening.
    • The findings suggest a significant advancement in developing practical deep learning tools for mammographic analysis.