دانلود مقاله انگلیسی رایگان:بهبود بهره وری گردش کار برای ماموگرافی با استفاده از یادگیری ماشین - 2019
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  • Improving Workflow Efficiency for Mammography Using Machine Learning Improving Workflow Efficiency for Mammography Using Machine Learning
    Improving Workflow Efficiency for Mammography Using Machine Learning

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Improving Workflow Efficiency for Mammography Using Machine Learning


    ترجمه فارسی عنوان مقاله:

    بهبود بهره وری گردش کار برای ماموگرافی با استفاده از یادگیری ماشین


    منبع:

    Sciencedirect - Elsevier - Journal of the American College of Radiology, Corrected proof: doi:10:1016/j:jacr:2019:05:012


    نویسنده:

    Trent Kyono, MSa, Fiona J. Gilbert, MBChBb,c, Mihaela van der Schaar, PhDa


    چکیده انگلیسی:

    Objective: The aim of this study was to determine whether machine learning could reduce the number of mammograms the radiologist must read by using a machine-learning classifier to correctly identify normal mammograms and to select the uncertain and abnormal examinations for radiological interpretation. Methods: Mammograms in a research data set from over 7,000 women who were recalled for assessment at six UK National Health Service Breast Screening Program centers were used. A convolutional neural network in conjunction with multitask learning was used to extract imaging features from mammograms that mimic the radiological assessment provided by a radiologist, the patient’s nonimaging features, and pathology outcomes. A deep neural network was then used to concatenate and fuse multiple mammogram views to predict both a diagnosis and a recommendation of whether or not additional radiological assessment was needed. Results: Ten-fold cross-validation was used on 2,000 randomly selected patients from the data set; the remainder of the data set was used for convolutional neural network training. While maintaining an acceptable negative predictive value of 0.99, the proposed model was able to identify 34% (95% confidence interval, 25%-43%) and 91% (95% confidence interval: 88%-94%) of the negative mammograms for test sets with a cancer prevalence of 15% and 1%, respectively. Conclusion: Machine learning was leveraged to successfully reduce the number of normal mammograms that radiologists need to read without degrading diagnostic accuracy.
    Key Words: Breast cancer | deep learning | machine learning | mammography | radiology


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 8
    حجم فایل: 408 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




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