Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks

OBJECTIVES: High breast density is a well-known risk factor for breast cancer. This study aimed to develop and adapt two (MLO, CC) deep convolutional neural networks (DCNN) for automatic breast density classification on synthetic 2D tomosynthesis reconstructions. METHODS: In total, 4605 synthetic 2D...

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Detalles Bibliográficos
Autores principales: Sexauer, Raphael, Hejduk, Patryk, Borkowski, Karol, Ruppert, Carlotta, Weikert, Thomas, Dellas, Sophie, Schmidt, Noemi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Breast
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10289992/
https://www.ncbi.nlm.nih.gov/pubmed/36856841
http://dx.doi.org/10.1007/s00330-023-09474-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10289992/
https://www.ncbi.nlm.nih.gov/pubmed/36856841
http://dx.doi.org/10.1007/s00330-023-09474-7

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