دانلود مقاله انگلیسی رایگان:یادگیری لنفوسیت ها در ایمونوهیستوشیمی با یادگیری عمیق - 2019
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دانلود مقاله انگلیسی یادگیری عمیق رایگان
  • Learning to detect lymphocytes in immunohistochemistry with deep learning Learning to detect lymphocytes in immunohistochemistry with deep learning
    Learning to detect lymphocytes in immunohistochemistry with deep learning

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Learning to detect lymphocytes in immunohistochemistry with deep learning


    ترجمه فارسی عنوان مقاله:

    یادگیری لنفوسیت ها در ایمونوهیستوشیمی با یادگیری عمیق


    منبع:

    Sciencedirect - Elsevier - Medical Image Analysis, 58 (2019) 101547: doi:10:1016/j:media:2019:101547


    نویسنده:

    Zaneta Swiderska-Chadaj a , ∗, Hans Pinckaers a , Mart van Rijthoven a , Maschenka Balkenhol a , Margarita Melnikova a , c , d , Oscar Geessink a , Quirine Manson e , Mark Sherman f , Antonio Polonia g , Jeremy Parry h , Mustapha Abubakar i , Geert Litjens a , Jeroen van der Laak a , b , Francesco Ciompi a


    چکیده انگلیسی:

    The immune system is of critical importance in the development of cancer. The evasion of destruction by the immune system is one of the emerging hallmarks of cancer. We have built a dataset of 171,166 manually annotated CD3 + and CD8 + cells, which we used to train deep learning algorithms for auto- matic detection of lymphocytes in histopathology images to better quantify immune response. Moreover, we investigate the effectiveness of four deep learning based methods when different subcompartments of the whole-slide image are considered: normal tissue areas, areas with immune cell clusters, and areas containing artifacts. We have compared the proposed methods in breast, colon and prostate cancer tissue slides collected from nine different medical centers. Finally, we report the results of an observer study on lymphocyte quantification, which involved four pathologists from different medical centers, and com- pare their performance with the automatic detection. The results give insights on the applicability of the proposed methods for clinical use. U-Net obtained the highest performance with an F1-score of 0.78 and the highest agreement with manual evaluation ( κ= 0 . 72 ), whereas the average pathologists agreement with reference standard was κ= 0 . 64 . The test set and the automatic evaluation procedure are publicly available at lyon19.grand-challenge.org .
    Keywords: Deep learning | Immune cell detection | Computational pathology | Immunohistochemistry


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 12
    حجم فایل: 3406 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




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