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Deep learning for bioimage analysis in developmental biology.
Hallou, Adrien; Yevick, Hannah G; Dumitrascu, Bianca; Uhlmann, Virginie.
Afiliação
  • Hallou A; Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, CB3 0HE, UK.
  • Yevick HG; Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, CB2 1QN, UK.
  • Dumitrascu B; Wellcome Trust/Medical Research Council Stem Cell Institute, University of Cambridge, Cambridge, CB2 1QR, UK.
  • Uhlmann V; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02142, USA.
Development ; 148(18)2021 09 15.
Article em En | MEDLINE | ID: mdl-34490888
ABSTRACT
Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Biologia do Desenvolvimento Limite: Humans / Male Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Biologia do Desenvolvimento Limite: Humans / Male Idioma: En Ano de publicação: 2021 Tipo de documento: Article