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دسته بندی:
یادگیری عمیق - deep learning
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
قیمت: رایگان
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