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Lymph Node Metastasis Prediction From Whole Slide Images With Transformer-Guided Multiinstance Learning and Knowledge

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    IEEE Transactions on Medical Imaging
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    This study introduces a novel transformer-guided framework for diagnosing papillary thyroid carcinoma lymph node metastasis from whole slide histopathological images. The method enhances accuracy by improving patch feature extraction and aggregation, outperforming existing approaches.

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    Area of Science:

    • Digital pathology
    • Computational oncology
    • Artificial intelligence in medicine

    Background:

    • Diagnosing lymph node metastasis in papillary thyroid carcinoma relies on analyzing whole slide histopathological images (WSIs), which are large and computationally intensive.
    • Existing computer-aided diagnosis methods often use multi-instance learning (MIL), but effectively aggregating information from image patches remains a challenge.

    Purpose of the Study:

    • To develop a novel transformer-guided framework for accurate prediction of lymph node metastasis from WSIs in papillary thyroid carcinoma.
    • To enhance the aggregation of patch-level information within WSIs for improved diagnostic accuracy.

    Main Methods:

    • A transformer-guided framework incorporating a lightweight feature extractor (Tiny-ViT) and clustering-based instance selection for discriminative patch feature extraction.
    • A Transformer-MIL module designed to capture relationships between sparse patches and aggregate features for slide-level prediction.
    • An attention-based mutual knowledge distillation (AMKD) paradigm to leverage pathological relationships and address limited slide-level annotations.

    Main Results:

    • The proposed Transformer-MIL framework effectively extracts and aggregates patch-level features from WSIs.
    • The AMKD paradigm improves model robustness by utilizing inter-pathological relationships.
    • The novel framework achieved an AUC improvement of over 2.72% compared to state-of-the-art methods on a collected WSI dataset.

    Conclusions:

    • The developed transformer-guided framework significantly improves the accuracy of diagnosing lymph node metastasis in papillary thyroid carcinoma from WSIs.
    • The integration of Transformer-MIL and attention-based knowledge distillation offers a promising approach for digital pathology image analysis.
    • This method represents a substantial advancement over current state-of-the-art techniques in the field.