Generative Adversarial Domain Adaptation for Nucleus Quantification in Images of Tissue Immunohistochemically Stained for Ki-67

PURPOSE: We focus on the problem of scarcity of annotated training data for nucleus recognition in Ki-67 immunohistochemistry (IHC)–stained pancreatic neuroendocrine tumor (NET) images. We hypothesize that deep learning–based domain adaptation is helpful for nucleus recognition when image annotation...

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Detalles Bibliográficos
Autores principales: Zhang, Xuhong, Cornish, Toby C., Yang, Lin, Bennett, Tellen D., Ghosh, Debashis, Xing, Fuyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society of Clinical Oncology 2020
Materias:
ORIGINAL REPORTS
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7397778/
https://www.ncbi.nlm.nih.gov/pubmed/32730116
http://dx.doi.org/10.1200/CCI.19.00108

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7397778/
https://www.ncbi.nlm.nih.gov/pubmed/32730116
http://dx.doi.org/10.1200/CCI.19.00108

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