دانلود مقاله انگلیسی رایگان:Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images - 2019
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  • Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images
    Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images


    ترجمه فارسی عنوان مقاله:

    Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images


    منبع:

    Sciencedirect - Elsevier - European Journal of Cancer, 118 (2019) 91-96: doi:10:1016/j:ejca:2019:06:012


    نویسنده:

    Achim Hekler a, Jochen S. Utikal b,c, Alexander H. Enk d, Wiebke Solass e, Max Schmitt a, Joachim Klode f, Dirk Schadendorf f, Wiebke Sondermann f, Cindy Franklin g, Felix Bestvater h, Michael J. Flaig i, Dieter Krahl j, Christof von Kalle a, Stefan Fro¨hling a, Titus J. Brinker


    چکیده انگلیسی:

    Abstract Background: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports on 25e26% of discordance for classifying a benign nevus versus malignant melanoma. A recent study indicated the potential of deep learning to lower these discordances. However, the performance of deep learning in classifying histopathologic melanoma images was never compared directly to human experts. The aim of this study is to perform such a first direct comparison. Methods: A total of 695 lesions were classified by an expert histopathologist in accordance with current guidelines (350 nevi/345 melanoma). Only the haematoxylin & eosin (H&E) slides of these lesions were digitalised via a slide scanner and then randomly cropped. A total of 595 of the resulting images were used to train a convolutional neural network (CNN). The additional 100 H&E image sections were used to test the results of the CNN in comparison to 11 histopathologists. Three combined McNemar tests comparing the results of the CNNs test runs in terms of sensitivity, specificity and accuracy were predefined to test for significance (p < 0.05). Findings: The CNN achieved a mean sensitivity/specificity/accuracy of 76%/60%/68% over 11 test runs. In comparison, the 11 pathologists achieved a mean sensitivity/specificity/accuracy of 51.8%/66.5%/59.2%. Thus, the CNN was significantly (p Z 0.016) superior in classifying the cropped images. Interpretation: With limited image information available, a CNN was able to outperform 11 histopathologists in the classification of histopathological melanoma images and thus shows promise to assist human melanoma diagnoses.
    KEYWORDS : Melanoma | Pathology | Histopathology | Deep learning | Artificial intelligence


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 6
    حجم فایل: 574 کیلوبایت

    قیمت: رایگان


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