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Consensus and Complementary Feature Guided Multi-modal Knowledge Distillation Network for Breast Cancer Diagnosis.

Shuyu Guo, Lan Huang, Ke Tao

    IEEE Journal of Biomedical and Health Informatics
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    This study introduces a two-stage framework for HER2 grading in breast cancer using only Hematoxylin and Eosin (H&E) images. It achieves comparable accuracy to multi-modal methods, making diagnostics more accessible.

    Area of Science:

    • Digital pathology
    • Computational oncology
    • Biomedical imaging analysis

    Background:

    • Accurate HER2 status assessment is crucial for breast cancer treatment decisions.
    • Hematoxylin and Eosin (H&E) and Immunohistochemistry (IHC) staining provide complementary data for HER2 evaluation.
    • IHC's cost and accessibility limitations hinder routine clinical use of multi-modal approaches.

    Purpose of the Study:

    • To develop an effective HER2 grading framework utilizing solely H&E images.
    • To enable precise HER2 status assessment without relying on expensive IHC.

    Main Methods:

    • A two-stage diagnostic framework involving Consensus and Complementary Feature Co-Embedding Network (CoCoFNet) and Hierarchical Multi-modal Knowledge Distillation (HM-KD).
    • CoCoFNet extracts modality-specific and cross-modal features for comprehensive representation.

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  • HM-KD transfers knowledge from a multi-modal teacher to a unimodal student network.

    • Main Results:

    • The proposed method achieves performance comparable to state-of-the-art distillation techniques for HER2 grading using only H&E images.
    • CoCoFNet enhances feature fusion, leading to improved supervision and generalization in the unimodal student network.
    • Experimental validation on two public datasets confirms the efficacy of the H&E-only approach.

    Conclusions:

    • The developed framework offers a cost-effective and accessible alternative for HER2 grading in breast cancer.
    • This approach democratizes precise HER2 status assessment, particularly in resource-limited settings.
    • The study highlights the potential of advanced deep learning techniques to overcome limitations in traditional diagnostic workflows.