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.

Nielsen, Patricia Switten; Georgsen, Jeanette Baehr; Vinding, Mads Sloth; Østergaard, Lasse Riis; Steiniche, Torben

Nielsen, Patricia Switten; Georgsen, Jeanette Baehr; Vinding, Mads Sloth; Østergaard, Lasse Riis; Steiniche, Torben.

Afiliação

Nielsen PS; Department of Pathology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 35, DK-8200 Aarhus, Denmark.
Georgsen JB; Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, DK-8200 Aarhus, Denmark.
Vinding MS; Department of Pathology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 35, DK-8200 Aarhus, Denmark.
Østergaard LR; Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, DK-8200 Aarhus, Denmark.
Steiniche T; Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, DK-8200 Aarhus, Denmark.

Int J Environ Res Public Health ; 19(21)2022 11 02.

Article em En | MEDLINE | ID: mdl-36361209

Assuntos

Melanoma; Neoplasias Cutâneas; Humanos; Carga Tumoral; Redes Neurais de Computação; Computadores; Fatores de Transcrição SOXE; Melanoma Maligno Cutâneo

Palavras-chave

H&E; IHC-supervised annotation; SOX10; artificial intelligence; deep learning; digital multiple stains; digital pathology; immunohistochemistry; melanoma; tumor burden

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Cutâneas / Melanoma Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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