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J Am Coll Radiol. 2019 May 30. pii: S1546-1440(19)30596-4. doi: 10.1016/j.jacr.2019.05.012. [Epub ahead of print]

Improving Workflow Efficiency for Mammography Using Machine Learning.

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Department of Computer Science, University of California Los Angeles, Los Angeles, California. Electronic address:
Department of Radiology, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom; NIHR Cambridge Biomedical Research Center, Cambridge, United Kingdom.
Department of Computer Science, University of California Los Angeles, Los Angeles, California.



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.


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.


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.


Machine learning was leveraged to successfully reduce the number of normal mammograms that radiologists need to read without degrading diagnostic accuracy.


Breast cancer; deep learning; machine learning; mammography; radiology


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