دانلود مقاله انگلیسی رایگان:یک سیستم با قابلیت یادگیری عمیق برای آزمایش غربالگری سریع بر روی ct قفسه سینه - 2019
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دانلود مقاله انگلیسی یادگیری عمیق رایگان
  • Deep learning-enabled system for rapid pneumothorax screening on chest CT Deep learning-enabled system for rapid pneumothorax screening on chest CT
    Deep learning-enabled system for rapid pneumothorax screening on chest CT

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Deep learning-enabled system for rapid pneumothorax screening on chest CT


    ترجمه فارسی عنوان مقاله:

    یک سیستم با قابلیت یادگیری عمیق برای آزمایش غربالگری سریع بر روی ct قفسه سینه


    منبع:

    Sciencedirect - Elsevier - European Journal of Radiology, 120 (2019) 108692: doi:10:1016/j:ejrad:2019:108692


    نویسنده:

    Xiang Li⁎, James H. Thrall, Subba R. Digumarthy, Mannudeep K. Kalra, Pari V. Pandharipande, Bowen Zhang, Chayanin Nitiwarangkul, Ramandeep Singh, Ruhani Doda Khera, Quanzheng Li


    چکیده انگلیسی:

    Purpose: Prompt diagnosis and quantitation of pneumothorax impact decisions pertaining to patient management. The purpose of our study was to develop and evaluate the accuracy of a deep learning (DL)-based image classification program for detection of pneumothorax on chest CT. Method: In an IRB approved study, an eight-layer convolutional neural network (CNN) using constant-size (36*36 pixels) 2D image patches was trained on a set of 80 chest CTs, with (n=50) and without (n=30) pneumothorax. Image patches were classified based on their probability of representing pneumothorax with subsequent generation of 3D heat-maps. The heat maps were further defined to include 1) pneumothorax area size, 2) relative location of the region to the lung boundary, and 3) a shape descriptor based on regional anisotropy. A support vector machine (SVM) was trained for classification. Result: We assessed performance of our program in a separate test dataset of 200 chest CT examinations, with (160/200, 75%) and without (40/200, 25%) pneumothorax. Data were analyzed to determine the accuracy, sensitivity, specificity. The subject-wise sensitivity was 100% (all 160/160 pneumothoraces detected) and specificity was 82.5% (33 true negative/40). False positive classifications were primarily related to emphysema and/or artifacts in the test images. Conclusion: This deep learning-based program demonstrated high accuracy for automatic detection of pneumothorax on chest CTs. By implementing it on a high-performance computing platform and integrating the domain knowledge of radiologists into the analytics framework, our method can be used to rapidly pre-screen large numbers of cases for presence of pneumothorax, a critical finding.
    Keywords: Chest CT | Pneumothorax | Deep learning


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 6
    حجم فایل: 802 کیلوبایت

    قیمت: رایگان


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