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MSA-GCN: A Multi-information Selection Aggregation Graph Convolutional Network for Breast Tumor Grading.

Kang Li, Suya Han, Lei Yang

    IEEE Journal of Biomedical and Health Informatics
    |October 26, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel Multi-information Selection Aggregation Graph Convolutional Networks (MSA-GCN) for breast tumor grading. Our method significantly improves diagnostic accuracy by integrating multi-modal data, outperforming existing approaches.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Oncology

    Background:

    • Breast tumor grading is crucial for treatment planning.
    • Current methods relying solely on imaging data have limited accuracy.
    • Integrating multi-modal data can enhance diagnostic capabilities.

    Purpose of the Study:

    • To develop an advanced breast tumor grading method using multi-modal data.
    • To improve the accuracy and reliability of breast tumor classification.
    • To address the limitations of image-only grading systems.

    Main Methods:

    • Proposed a Multi-information Selection Aggregation Graph Convolutional Networks (MSA-GCN).
    • Developed an automatic screening and weight encoder for phenotypic data to construct population graphs.
    • Employed similarity learning for patient image feature correlation and a multi-information selection aggregation mechanism for feature extraction.

    Main Results:

    • Achieved average classification accuracies of 90.74% on the DDSM dataset and 85.35% on the INbreast dataset.
    • Demonstrated superior performance compared to existing breast tumor grading methods.
    • Effectively fused image and non-image (phenotypic) information for enhanced classification.

    Conclusions:

    • The proposed MSA-GCN method significantly improves breast tumor grading accuracy.
    • Effective fusion of multi-modal data is key to enhancing diagnostic performance.
    • This approach offers a promising advancement in breast cancer diagnosis.