Determination of mammographic breast density using a deep convolutional neural network.

Journal: The British journal of radiology

Published Date: Jan 1, 2019

Abstract

OBJECTIVE: High breast density is a risk factor for breast cancer. The aim of this study was to develop a deep convolutional neural network (dCNN) for the automatic classification of breast density based on the mammographic appearance of the tissue according to the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) Atlas.

Authors

Alexander Ciritsis

From the *Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland.
Cristina Rossi
Ilaria Vittoria De Martini

Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zürich, Switzerland.
Matthias Eberhard

Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zürich, Switzerland.
Magda Marcon
Anton S Becker

From the Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland.
Nicole Berger

Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zürich, Switzerland.
Andreas Boss

Keywords

Adult Aged Breast Breast Density Breast Neoplasms Cohort Studies Deep Learning Female Humans Image Processing, Computer-Assisted Mammography Middle Aged Neural Networks, Computer Observer Variation Precancerous Conditions Retrospective Studies ROC Curve Switzerland

External Resources

View on PubMed Access via DOI PubMed (30209957)

Determination of mammographic breast density using a deep convolutional neural network.

Abstract

Authors

Keywords

External Resources

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