Fast Analysis of Time-Domain Fluorescence Lifetime Imaging via Extreme Learning Machine.

Zang, Zhenya; Xiao, Dong; Wang, Quan; Li, Zinuo; Xie, Wujun; Chen, Yu; Li, David Day Uei

Zang, Zhenya; Xiao, Dong; Wang, Quan; Li, Zinuo; Xie, Wujun; Chen, Yu; Li, David Day Uei.

Afiliação

Zang Z; Department of Biomedical Engineering, University of Strathclyde, Glasgow G4 0RE, UK.
Xiao D; Department of Biomedical Engineering, University of Strathclyde, Glasgow G4 0RE, UK.
Wang Q; Department of Biomedical Engineering, University of Strathclyde, Glasgow G4 0RE, UK.
Li Z; Department of Physics, University of Strathclyde, Glasgow G4 0NG, UK.
Xie W; Department of Biomedical Engineering, University of Strathclyde, Glasgow G4 0RE, UK.
Chen Y; Department of Physics, University of Strathclyde, Glasgow G4 0NG, UK.
Li DDU; Department of Biomedical Engineering, University of Strathclyde, Glasgow G4 0RE, UK.

Sensors (Basel) ; 22(10)2022 May 15.

Article em En | MEDLINE | ID: mdl-35632167

Assuntos

Algoritmos; Redes Neurais de Computação; Fluorescência; Humanos; Masculino

Palavras-chave

computational imaging; fluorescence lifetime imaging microscopy; machine learning; single-photon time-correlated counting (TCSPC)

Texto completo

Adicionar na Minha BVS

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Redes Neurais de Computação Limite: Humans / Male Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Reino Unido

Texto completo

Adicionar na Minha BVS

Imprimir

XML

PubMed Links

Buscar no Google