A novel deep learning architecture outperforming ‘off-the-shelf’ transfer learning and feature-based methods in the automated assessment of mammographic breast density

Potentially suspicious breast neoplasms could be masked by high tissue density, thus increasing the probability of a false-negative diagnosis. Furthermore, differentiating breast tissue type enables patient pre-screening stratification and risk assessment. In this study, we propose and evaluate adva...

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Detalles Bibliográficos
Autores principales: Trivizakis, Eleftherios, Ioannidis, Georgios S., Melissianos, Vasileios D., Papadakis, Georgios Z., Tsatsakis, Aristidis, Spandidos, Demetrios A., Marias, Kostas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2019
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6787954/
https://www.ncbi.nlm.nih.gov/pubmed/31545461
http://dx.doi.org/10.3892/or.2019.7312

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6787954/
https://www.ncbi.nlm.nih.gov/pubmed/31545461
http://dx.doi.org/10.3892/or.2019.7312

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