Deep ensemble learning enables highly accurate classification of stored red blood cell morphology.

Routt, Austin H; Yang, Natalia; Piety, Nathaniel Z; Lu, Madeleine; Shevkoplyas, Sergey S

Routt, Austin H; Yang, Natalia; Piety, Nathaniel Z; Lu, Madeleine; Shevkoplyas, Sergey S.

Afiliação

Routt AH; Department of Biomedical Engineering, Cullen College of Engineering, University of Houston, 3605 Cullen Blvd, Houston, TX, 77204-5060, USA.
Yang N; Department of Biomedical Engineering, Cullen College of Engineering, University of Houston, 3605 Cullen Blvd, Houston, TX, 77204-5060, USA.
Piety NZ; Department of Biomedical Engineering, Cullen College of Engineering, University of Houston, 3605 Cullen Blvd, Houston, TX, 77204-5060, USA.
Lu M; Department of Biomedical Engineering, Cullen College of Engineering, University of Houston, 3605 Cullen Blvd, Houston, TX, 77204-5060, USA.
Shevkoplyas SS; Department of Biomedical Engineering, Cullen College of Engineering, University of Houston, 3605 Cullen Blvd, Houston, TX, 77204-5060, USA. sshevkoplyas@uh.edu.

Sci Rep ; 13(1): 3152, 2023 02 23.

Article em En | MEDLINE | ID: mdl-36823298

Assuntos

Eritrócitos Anormais; Eritrócitos; Humanos; Reprodutibilidade dos Testes; Testes Hematológicos; Aprendizado de Máquina

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Eritrócitos / Eritrócitos Anormais Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Reino Unido

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