Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists

Numerous cancer histopathology specimens have been collected and digitized over the past few decades. A comprehensive evaluation of the distribution of various cells in tumor tissue sections can provide valuable information for understanding cancer. Deep learning is suitable for achieving these goal...

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Detalles Bibliográficos
Autores principales: Komura, Daisuke, Onoyama, Takumi, Shinbo, Koki, Odaka, Hiroto, Hayakawa, Minako, Ochi, Mieko, Herdiantoputri, Ranny Rahaningrum, Endo, Haruya, Katoh, Hiroto, Ikeda, Tohru, Ushiku, Tetsuo, Ishikawa, Shumpei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Descriptor
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9982301/
https://www.ncbi.nlm.nih.gov/pubmed/36873900
http://dx.doi.org/10.1016/j.patter.2023.100688

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9982301/
https://www.ncbi.nlm.nih.gov/pubmed/36873900
http://dx.doi.org/10.1016/j.patter.2023.100688

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