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
Sciencedirect - Elsevier - Journal of the American College of Radiology, Corrected proof: doi:10:1016/j:jacr:2019:05:012
Trent Kyono, MSa, Fiona J. Gilbert, MBChBb,c, Mihaela van der Schaar, PhDa
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