Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, CB3 0HE, UK.
Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, CB2 1QN, UK.
Development. 2021 Sep 15;148(18). doi: 10.1242/dev.199616. Epub 2021 Sep 7.
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.
深度学习改变了处理大型复杂图像数据集的方式，重塑了生物图像分析的可能性。随着生物图像数据的复杂性和规模不断增长，这种新的分析范例变得越来越普遍。在这篇综述中，我们首先介绍初学者理解深度学习所需的概念。然后，我们回顾了深度学习如何影响生物图像分析，并探讨了可用于将其集成到研究项目中的开源资源。最后，我们讨论了深度学习在细胞和发育生物学中的应用前景。我们分析了最先进的方法如何通过新的基于图像的分析和建模来改变我们对生物系统的理解，这些方法将时空上的多模态输入进行整合。
