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Multiple-instance learning for breast cancer detection in mammograms.

Rubén Sánchez de la Rosa, Mathieu Lamard, Guy Cazuguel

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
    Summary
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    This study introduces a computer-aided system for breast cancer detection using mammography and Multiple-Instance Learning (MIL). The system achieved 91.1% accuracy in recognizing normal mammograms, aiding in early breast cancer diagnosis.

    Area of Science:

    • Medical Imaging
    • Machine Learning
    • Oncology

    Background:

    • Breast cancer is the most common cancer in women.
    • Mammography is a key tool for breast cancer screening.
    • Computer-aided detection and diagnosis systems can improve diagnostic accuracy.

    Purpose of the Study:

    • To develop and evaluate an experimental computer-aided detection and diagnosis system for breast cancer using mammography.
    • To leverage the Multiple-Instance Learning (MIL) paradigm for improved medical decision support.
    • To accurately classify mammographic examinations as normal, benign, or cancerous.

    Main Methods:

    • Adaptive partitioning of breasts into regions.
    • Extraction of textural features and features from mass and microcalcification detection.

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  • Application of MIL algorithms (Citation k-NN, mi-Graph) for feature vector combination.
  • Utilizing the DDSM dataset for system validation.

    • Main Results:

    • Achieved 91.1% accuracy for normality recognition.
    • Achieved 62.1% accuracy for three-class categorization (normal, benign, cancer).
    • Demonstrated the efficacy of the MIL paradigm in breast cancer diagnosis.

    Conclusions:

    • The developed MIL-based system shows promise for computer-aided breast cancer detection and diagnosis.
    • Future work will focus on enhancing benign versus cancer discrimination.
    • Further optimization of the MIL paradigm is expected to improve diagnostic performance.