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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.

Author information

1
Department of Computer Science, University of California Los Angeles, Los Angeles, California. Electronic address: tmkyono@gmail.com.
2
Department of Radiology, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom; NIHR Cambridge Biomedical Research Center, Cambridge, United Kingdom.
3
Department of Computer Science, University of California Los Angeles, Los Angeles, California.

Abstract

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.

KEYWORDS:

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

PMID:
31153798
DOI:
10.1016/j.jacr.2019.05.012

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