Automatic and standardized quality assurance of digital mammography and tomosynthesis with deep convolutional neural networks

Automatic and standardized quality assurance of digital mammography and tomosynthesis with deep convolutional neural networks

Abstract Objectives The aim of this study was to develop and validate a commercially available AI platform for the automatic determination of image quality in mammography and tomosynthesis considering a standardized set of features. Materials and methods In this retrospective study, 11,733 mammogram...

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Bibliographic Details
Main Authors: Patryk Hejduk, Raphael Sexauer, Carlotta Ruppert, Karol Borkowski, Jan Unkelbach, Noemi Schmidt
Format: Article
Language:English
Published: SpringerOpen 2023-05-01
Series:Insights into Imaging
Subjects:
Mammography
Tomosynthesis
Quality control
Machine learning
Artificial intelligence
Online Access:https://doi.org/10.1186/s13244-023-01396-8

Internet

https://doi.org/10.1186/s13244-023-01396-8

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