Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making.

Journal: European radiology

Published Date: Mar 29, 2019

Abstract

OBJECTIVES: To evaluate a deep convolutional neural network (dCNN) for detection, highlighting, and classification of ultrasound (US) breast lesions mimicking human decision-making according to the Breast Imaging Reporting and Data System (BI-RADS).

Authors

Alexander Ciritsis

From the *Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland.
Cristina Rossi
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.
Andreas Boss

Keywords

Adult Aged Algorithms Breast Neoplasms Clinical Decision-Making Female Humans Image Interpretation, Computer-Assisted Middle Aged Neural Networks, Computer Observer Variation Retrospective Studies ROC Curve Ultrasonography, Mammary

External Resources

View on PubMed Access via DOI PubMed (30927100)

Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making.

Abstract

Authors

Keywords

External Resources

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