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Deep learning for bioimage analysis in developmental biology
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 introdu...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Company of Biologists Ltd
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8451066/ https://www.ncbi.nlm.nih.gov/pubmed/34490888 http://dx.doi.org/10.1242/dev.199616 |
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author | Hallou, Adrien Yevick, Hannah G. Dumitrascu, Bianca Uhlmann, Virginie |
author_facet | Hallou, Adrien Yevick, Hannah G. Dumitrascu, Bianca Uhlmann, Virginie |
author_sort | Hallou, Adrien |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8451066 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Company of Biologists Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-84510662021-09-21 Deep learning for bioimage analysis in developmental biology Hallou, Adrien Yevick, Hannah G. Dumitrascu, Bianca Uhlmann, Virginie Development Review 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. The Company of Biologists Ltd 2021-09-07 /pmc/articles/PMC8451066/ /pubmed/34490888 http://dx.doi.org/10.1242/dev.199616 Text en © 2021. Published by The Company of Biologists Ltd https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. |
spellingShingle | Review Hallou, Adrien Yevick, Hannah G. Dumitrascu, Bianca Uhlmann, Virginie Deep learning for bioimage analysis in developmental biology |
title | Deep learning for bioimage analysis in developmental biology |
title_full | Deep learning for bioimage analysis in developmental biology |
title_fullStr | Deep learning for bioimage analysis in developmental biology |
title_full_unstemmed | Deep learning for bioimage analysis in developmental biology |
title_short | Deep learning for bioimage analysis in developmental biology |
title_sort | deep learning for bioimage analysis in developmental biology |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8451066/ https://www.ncbi.nlm.nih.gov/pubmed/34490888 http://dx.doi.org/10.1242/dev.199616 |
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