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Related Concept Videos

Larynx01:21

Larynx

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The human larynx, often referred to as the voice box, is an intricate organ located in the neck. It serves as a pathway for air to enter the lungs during respiration and is an essential component of voice production.
Anatomy of the Larynx
The larynx consists of various components, including cartilage, muscles, and vocal cords. Its structure includes three large unpaired cartilages—the thyroid, cricoid, and epiglottis—and three smaller paired cartilages—the arytenoids,...
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Knowledge-Driven Multiple Instance Learning With Hierarchical Cluster-Incorporated Aware Filtering for Larynx

Chentao Li, Pan Huang, Jing Qin

    IEEE Journal of Biomedical and Health Informatics
    |September 15, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces HCF-MIL, a new AI method for grading laryngeal squamous cell carcinoma (LSCC) using whole-slide images (WSIs). HCF-MIL improves grading accuracy and interpretability by focusing on tumor regions, aiding pathologists in diagnosis.

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    Area of Science:

    • Oncology
    • Digital Pathology
    • Artificial Intelligence in Medicine

    Background:

    • Pathological grading of laryngeal squamous cell carcinoma (LSCC) is vital for patient outcomes.
    • Current multiple instance learning (MIL) methods struggle with accurate grading due to over-reliance on non-tumor regions and background noise in whole-slide images (WSIs).
    • This leads to suboptimal performance and reduced interpretability in automated WSIs analysis.

    Purpose of the Study:

    • To develop an advanced AI model for precise pathological grading of LSCC from WSIs.
    • To enhance the interpretability of AI-driven pathological analysis, aligning it with expert pathologist decision-making.
    • To improve the reliability of automated grading for clinical applications.

    Main Methods:

    • Proposed HCF-MIL (Hierarchical Cluster-incorporated Aware Filtering MIL) network, an end-to-end knowledge-driven approach.
    • Implemented tumor-guiding cluster filtering to focus on relevant tumor instances and filter irrelevant information.
    • Introduced an enhanced filtering aggregation strategy for robust hierarchical feature representation.

    Main Results:

    • HCF-MIL significantly improved pathological grading performance on both larynx and multicentre datasets.
    • The method demonstrated enhanced interpretability, better aligning AI decisions with pathologist behavior.
    • Achieved reduced model entropy, indicating a more focused and reliable decision-making process.

    Conclusions:

    • HCF-MIL offers a significant advancement in automated LSCC grading using WSIs.
    • The knowledge-driven approach enhances both accuracy and interpretability, crucial for clinical deployment.
    • This work provides a robust foundation for AI-assisted pathological diagnosis in oncology.