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...

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Detalles Bibliográficos
Autores principales: Yin, Peng-Nien, KC, Kishan, Wei, Shishi, Yu, Qi, Li, Rui, Haake, Anne R., Miyamoto, Hiroshi, Cui, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research Article
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

Internet

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

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