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CPGNet: Multimodal Graph Learning with Hierarchical Category Guidance for Multi-Label Whole Slide Image

Haoyun Zhao, Dapeng Tao, Yibing Zhan

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    CPGNet, a novel multi-label Whole Slide Image (WSI) classifier, addresses real-world cancer subtype challenges. This category-prompted graph network improves diagnostic accuracy in digital pathology.

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    Area of Science:

    • Digital Pathology
    • Computational Pathology
    • Artificial Intelligence in Medicine

    Background:

    • Current automated cancer type identification in Whole Slide Images (WSIs) uses single-label classification, which is insufficient for complex clinical scenarios.
    • Real-world digital pathology data often features multi-label characteristics and class imbalance, challenging existing automated methods.
    • Accurate WSI analysis is crucial for cancer diagnosis, treatment planning, and prognosis.

    Purpose of the Study:

    • To develop an advanced multi-label Whole Slide Image (WSI) classifier, CPGNet, to better handle complex cancer subtypes and class imbalance in digital pathology.
    • To mimic the diagnostic process of pathologists by integrating local and global feature extraction and leveraging semantic category relationships.
    • To improve the accuracy and robustness of automated cancer subtyping in clinical settings.

    Main Methods:

    • CPGNet utilizes MaskSLIC for superpixel segmentation of WSIs, representing them as graphs with nodes and edges.
    • A Graph Neural Network (GNN) with a multi-head self-attention mechanism (GLGFI module) captures local and global spatial dependencies.
    • A Visual-Category Interaction (VCI) module leverages semantic relationships, and a reweighting strategy addresses class imbalance.

    Main Results:

    • CPGNet demonstrated superior performance, universality, and robustness across private (YNLUAD) and public (BCNB, AGGC22) datasets.
    • The proposed multi-label classification approach effectively handles the complexities of real-world cancer subtype identification.
    • The model successfully captures intricate spatial distributions and mimics expert pathologist diagnostic strategies.

    Conclusions:

    • CPGNet offers a significant advancement in automated multi-label WSI classification for digital pathology.
    • The model's ability to handle class imbalance and complex spatial relationships enhances its clinical applicability.
    • This approach provides a more realistic and effective tool for cancer diagnosis and prognosis support.