DeepCell Kiosk: scaling deep learning-enabled cellular image analysis with Kubernetes.

DeepCell Kiosk: scaling deep learning-enabled cellular image analysis with Kubernetes.
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DOI:
10.1038/s41592-020-01023-0
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发表时间:
2021-01
期刊:
影响因子:
48
通讯作者:
Van Valen D
Van Valen D
中科院分区:
生物学1区
文献类型:
--
作者:
Bannon D;Moen E;Schwartz M;Borba E;Kudo T;Greenwald N;Vijayakumar V;Chang B;Pao E;Osterman E;Graf W;Van Valen D

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深度学习正在改变生物图像的分析,但将这些模型应用于大型数据集仍然具有挑战性。在此我们描述了DeepCell Kiosk,这是一种原生云软件,它能动态扩展深度学习工作流程以适应大型成像数据集。为了证明该软件的可扩展性和经济性,我们在约5.5小时内以约250美元的成本在106张100万像素的图像中识别出了细胞核,根据集群配置不同,成本可降至100美元以下。DeepCell Kiosk可在https://github.com/vanvalenlab/kiosk - console下载;在https://deepcell.org/可获取持久部署版本。
Deep learning is transforming the analysis of biological images, but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 106 1-megapixel images in ~5.5 h for ~US$250, with a cost below US$100 achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at https://github.com/vanvalenlab/kiosk-console; a persistent deployment is available at https://deepcell.org/.
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