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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2018
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| Materias: | |
| 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 |