Journal
DEVELOPMENT
Volume 148, Issue 18, Pages -Publisher
COMPANY BIOLOGISTS LTD
DOI: 10.1242/dev.199616
Keywords
Deep learning; Neural network; Image analysis; Microscopy; Bioimaging
Categories
Funding
- Wellcome Trust through a Junior Interdisciplinary Research Fellowship [098357/Z/12/Z]
- University of Cambridge through a Herchel Smith Postdoctoral Research Fellowship
- National Institute of General Medical Sciences [K99GM136915]
- Accelerate Programme for Scientific Discovery
- European Molecular Biology Laboratory
- Wellcome Trust/CRUK Gurdon Institute [203144/Z/16/Z, C6946/A24843]
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Deep learning has transformed the way large and complex image datasets can be processed in bioimage analysis. This new paradigm is becoming increasingly ubiquitous as bioimage data continues to grow. State-of-the-art methodologies in deep learning have the potential to revolutionize our understanding of biological systems through new image-based analysis and modelling.
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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