A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification

Completely labeled pathology datasets are often challenging and time-consuming to obtain. Semi-supervised learning (SSL) methods are able to learn from fewer labeled data points with the help of a large number of unlabeled data points. In this paper, we investigated the possibility of using clusteri...

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Detalles Bibliográficos
Autores principales: Peikari, Mohammad, Salama, Sherine, Nofech-Mozes, Sharon, Martel, Anne L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5940864/
https://www.ncbi.nlm.nih.gov/pubmed/29739993
http://dx.doi.org/10.1038/s41598-018-24876-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5940864/
https://www.ncbi.nlm.nih.gov/pubmed/29739993
http://dx.doi.org/10.1038/s41598-018-24876-0

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