Structured crowdsourcing enables convolutional segmentation of histology images

MOTIVATION: While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate ti...

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Detalles Bibliográficos
Autores principales: Amgad, Mohamed, Elfandy, Habiba, Hussein, Hagar, Atteya, Lamees A, Elsebaie, Mai A T, Abo Elnasr, Lamia S, Sakr, Rokia A, Salem, Hazem S E, Ismail, Ahmed F, Saad, Anas M, Ahmed, Joumana, Elsebaie, Maha A T, Rahman, Mustafijur, Ruhban, Inas A, Elgazar, Nada M, Alagha, Yahya, Osman, Mohamed H, Alhusseiny, Ahmed M, Khalaf, Mariam M, Younes, Abo-Alela F, Abdulkarim, Ali, Younes, Duaa M, Gadallah, Ahmed M, Elkashash, Ahmad M, Fala, Salma Y, Zaki, Basma M, Beezley, Jonathan, Chittajallu, Deepak R, Manthey, David, Gutman, David A, Cooper, Lee A D
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Original Papers
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6748796/
https://www.ncbi.nlm.nih.gov/pubmed/30726865
http://dx.doi.org/10.1093/bioinformatics/btz083

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6748796/
https://www.ncbi.nlm.nih.gov/pubmed/30726865
http://dx.doi.org/10.1093/bioinformatics/btz083

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