Cell Detection Using Extremal Regions in a Semisupervised Learning Framework

This paper discusses an algorithm to build a semisupervised learning framework for detecting cells. The cell candidates are represented as extremal regions drawn from a hierarchical image representation. Training a classifier for cell detection using supervised approaches relies on a large amount of...

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Detalles Bibliográficos
Autores principales: Ramesh, Nisha, Liu, Ting, Tasdizen, Tolga
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5488495/
https://www.ncbi.nlm.nih.gov/pubmed/29065596
http://dx.doi.org/10.1155/2017/4080874

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5488495/
https://www.ncbi.nlm.nih.gov/pubmed/29065596
http://dx.doi.org/10.1155/2017/4080874

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