Improved breast cancer histological grading using deep learning.

Wang, Y; Acs, B; Robertson, S; Liu, B; Solorzano, L; Wählby, C; Hartman, J; Rantalainen, M

Wang, Y; Acs, B; Robertson, S; Liu, B; Solorzano, L; Wählby, C; Hartman, J; Rantalainen, M.

Afiliación

Wang Y; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Acs B; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden.
Robertson S; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden.
Liu B; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Solorzano L; Department of Information Technology and SciLifeLab, Uppsala University, Uppsala, Sweden.
Wählby C; Department of Information Technology and SciLifeLab, Uppsala University, Uppsala, Sweden.
Hartman J; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden; MedTechLabs, BioClinicum, Karolinska University Hospital, Solna, Sweden.
Rantalainen M; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; MedTechLabs, BioClinicum, Karolinska University Hospital, Solna, Sweden. Electronic address: mattias.rantalainen@ki.se.

Ann Oncol ; 33(1): 89-98, 2022 01.

Article en En | MEDLINE | ID: mdl-34756513

Asunto(s)

Neoplasias de la Mama; Aprendizaje Profundo; Neoplasias de la Mama/patología; Femenino; Humanos; Clasificación del Tumor; Pronóstico; Modelos de Riesgos Proporcionales; Estudios Retrospectivos

Palabras clave

artificial intelligence; breast cancer; deep learning; digital pathology; histological grade

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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Aprendizaje Profundo Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans Idioma: En Revista: Ann Oncol Asunto de la revista: NEOPLASIAS Año: 2022 Tipo del documento: Article País de afiliación: Suecia

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