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    Area of Science:

    • Digital Pathology
    • Computer Vision
    • Medical Image Analysis

    Background:

    • Weakly supervised semantic segmentation (WSSS) is crucial for segmenting histopathological whole slide images (WSI), reducing pathologist workload.
    • Current WSSS methods struggle with non-predominant tissue categories due to data imbalance and feature similarity.

    Purpose of the Study:

    • To develop advanced WSSS techniques for accurate histopathological tissue segmentation.
    • To address challenges in segmenting tail classes and improve overall segmentation performance.

    Main Methods:

    • Proposed three novel designs: Diffusion-based Data Generation, Feature Recalibration, and Grade-skip Learning.
    • Developed a comprehensive pipeline named LoHo for histopathology tissue segmentation.
    • Integrated these methods into a WSSS framework.

    Main Results:

    • Achieved new state-of-the-art performance in histopathology tissue segmentation.
    • Significantly improved the segmentation accuracy of tail classes.
    • Demonstrated the plug-and-play nature of the proposed methods for easy integration.

    Conclusions:

    • The developed LoHo pipeline and its integrated methods effectively address WSSS challenges in histopathology.
    • The approach enhances segmentation accuracy, particularly for underrepresented tissue categories.
    • The methods are versatile and can be integrated into existing WSSS frameworks.