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Enhanced Pathology Image Quality with Restore–Generative Adversarial Network

  • Ruichen Rong
    Affiliations
    Quantitative Biomedical Research Center, Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas
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  • Shidan Wang
    Affiliations
    Quantitative Biomedical Research Center, Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas
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  • Xinyi Zhang
    Affiliations
    Quantitative Biomedical Research Center, Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas
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  • Zhuoyu Wen
    Affiliations
    Quantitative Biomedical Research Center, Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas
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  • Xian Cheng
    Affiliations
    Quantitative Biomedical Research Center, Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas
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  • Liwei Jia
    Affiliations
    Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas
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  • Donghan M. Yang
    Affiliations
    Quantitative Biomedical Research Center, Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas
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  • Yang Xie
    Affiliations
    Quantitative Biomedical Research Center, Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas

    Department of Pathology, UT Southwestern Medical Center, Dallas, Texas

    Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas
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  • Xiaowei Zhan
    Affiliations
    Quantitative Biomedical Research Center, Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas

    Department of Pathology, UT Southwestern Medical Center, Dallas, Texas

    Center for the Genetics of Host Defense, UT Southwestern Medical Center, Dallas, Texas
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  • Guanghua Xiao
    Correspondence
    Address correspondence to Guanghua Xiao, Quantitative Biomedical Research Center, Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX.
    Affiliations
    Quantitative Biomedical Research Center, Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas

    Department of Pathology, UT Southwestern Medical Center, Dallas, Texas

    Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas
    Search for articles by this author
Published:January 17, 2023DOI:https://doi.org/10.1016/j.ajpath.2022.12.011
      Whole slide imaging is becoming a routine procedure in clinical diagnosis. Advanced image analysis techniques have been developed to assist pathologists in disease diagnosis, staging, subtype classification, and risk stratification. Recently, deep learning algorithms have achieved state-of-the-art performances in various imaging analysis tasks, including tumor region segmentation, nuclei detection, and disease classification. However, these algorithms are not widely applied in clinical applications, because their performances often degrade as a result of image quality issues, which are commonly seen in real-world pathology imaging data, such as low resolution, blurring regions, and staining variation. To resolve these challenges, we developed Restore–Generative Adversarial Network (GAN), a deep learning model to improve the imaging qualities by restoring blurred regions, enhancing low resolution, and normalizing staining colors. Our results demonstrate that Restore-GAN can significantly improve image quality, which leads to improved model robustness and performance for existing deep learning algorithms in pathology image analysis. We envision that Restore-GAN can be used to facilitate the applications of deep learning models in digital pathology analyses.
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