Computer-Assisted Annotation of Digital H&E/SOX10 Dual Stains Generates High-Performing Convolutional Neural Network for Calculating Tumor Burden in H&E-Stained Cutaneous Melanoma

Deep learning for the analysis of H&E stains requires a large annotated training set. This may form a labor-intensive task involving highly skilled pathologists. We aimed to optimize and evaluate computer-assisted annotation based on digital dual stains of the same tissue section. H&E stains...

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Detalles Bibliográficos
Autores principales: Nielsen, Patricia Switten, Georgsen, Jeanette Baehr, Vinding, Mads Sloth, Østergaard, Lasse Riis, Steiniche, Torben
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9654525/
https://www.ncbi.nlm.nih.gov/pubmed/36361209
http://dx.doi.org/10.3390/ijerph192114327

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9654525/
https://www.ncbi.nlm.nih.gov/pubmed/36361209
http://dx.doi.org/10.3390/ijerph192114327

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