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    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
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    This study introduces USIGAN, a new method for virtual immunohistochemical (IHC) staining from H&E images. It improves pathological semantic consistency, even with challenging spatial variations in weakly paired data.

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    Area of Science:

    • Digital pathology
    • Computational imaging
    • Medical artificial intelligence

    Background:

    • Virtual immunohistochemical (IHC) staining aims to generate IHC images from H&E images for efficient pathological analysis.
    • Weakly paired conditions and spatial heterogeneity between adjacent tissue slices pose challenges, leading to semantic inconsistencies.

    Purpose of the Study:

    • To develop a novel method for IHC virtual staining that addresses the limitations of weakly paired data and spatial heterogeneity.
    • To improve the pathological semantic consistency and content accuracy of generated virtual IHC images.

    Main Methods:

    • Proposed USIGAN (Unbalanced Self-Information Generative Adversarial Network) utilizing unbalanced self-information feature transport.
    • Implemented Unbalanced Optimal Transport Consistency Mining (UOT-CTM) and Pathology Self-Correspondence Mining (PC-SCM) mechanisms.
    • Extracted global morphological semantics independent of positional correspondence to mitigate weak pairing effects.

    Main Results:

    • USIGAN significantly improved content consistency and pathological semantic consistency compared to existing methods.
    • The method demonstrated superior performance in clinically relevant metrics like IoD and Pearson-R correlation on public datasets.
    • Generated virtual IHC images showed enhanced clinical relevance and accuracy.

    Conclusions:

    • USIGAN offers an effective solution for IHC virtual staining under challenging weakly paired conditions.
    • The proposed mechanisms successfully address spatial heterogeneity and improve semantic consistency in generated images.
    • This approach holds promise for advancing digital pathology and cost-effective analysis.