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Histopathological distinction of non-invasive and invasive bladder cancers using machine learning approaches
BACKGROUND: One of the most challenging tasks for bladder cancer diagnosis is to histologically differentiate two early stages, non-invasive Ta and superficially invasive T1, the latter of which is associated with a significantly higher risk of disease progression. Indeed, in a considerable number o...
| Autores principales: | , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7367328/ https://www.ncbi.nlm.nih.gov/pubmed/32680493 http://dx.doi.org/10.1186/s12911-020-01185-z |