Deep learning based digital cell profiles for risk stratification of urine cytology images.

Awan, Ruqayya; Benes, Ksenija; Azam, Ayesha; Song, Tzu-Hsi; Shaban, Muhammad; Verrill, Clare; Tsang, Yee Wah; Snead, David; Minhas, Fayyaz; Rajpoot, Nasir

Awan, Ruqayya; Benes, Ksenija; Azam, Ayesha; Song, Tzu-Hsi; Shaban, Muhammad; Verrill, Clare; Tsang, Yee Wah; Snead, David; Minhas, Fayyaz; Rajpoot, Nasir.

Afiliação

Awan R; Department of Computer Science, University of Warwick, Coventry, UK.
Benes K; The Royal Wolverhampton NHS Trust, Wolverhampton, UK.
Azam A; Department of Computer Science, University of Warwick, Coventry, UK.
Song TH; Department of Pathology, University Hospitals Coventry and Warwickshire, Coventry, UK.
Shaban M; Department of Computer Science, University of Warwick, Coventry, UK.
Verrill C; Laboratory of Quantitative Cellular Imaging, Worcester Polytechnic Institute, Worcester, Massachusetts, USA.
Tsang YW; Department of Computer Science, University of Warwick, Coventry, UK.
Snead D; Nuffield Department of Surgical Sciences and Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK.
Minhas F; Department of Pathology, University Hospitals Coventry and Warwickshire, Coventry, UK.
Rajpoot N; Department of Pathology, University Hospitals Coventry and Warwickshire, Coventry, UK.

Cytometry A ; 99(7): 732-742, 2021 07.

Article em En | MEDLINE | ID: mdl-33486882

Assuntos

Aprendizado Profundo; Citodiagnóstico; Curva ROC; Medição de Risco

Palavras-chave

The Paris System; cell classification; cell detection; cell segmentation; deep learning; digital risk; oversampling; urine cytology

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

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