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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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| Gepubliceerd in: | Oncol Rep |
|---|---|
| Hoofdauteurs: | , , , , , , |
| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
D.A. Spandidos
2019
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| Onderwerpen: | |
| Online toegang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6787954/ https://ncbi.nlm.nih.gov/pubmed/31545461 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3892/or.2019.7312 |
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