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DeepCell 2.0: Automated cloud deployment of deep learning models for large-scale cellular image analysis

Bannon, Dylan and Moen, Erick and Borba, Enrico and Ho, Andrew and Camplisson, Isabella and Chang, Brian and Osterman, Eric and Graf, William and Van Valen, David (2018) DeepCell 2.0: Automated cloud deployment of deep learning models for large-scale cellular image analysis. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190102-092232849

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Abstract

Deep learning is transforming the ability of life scientists to extract information from images. While these techniques have superior accuracy in comparison to conventional approaches and enable previously impossible analyses, their unique hardware and software requirements have prevented widespread adoption by life scientists. To meet this need, we have developed DeepCell 2.0, an open source library for training and delivering deep learning models with cloud computing. This library enables users to configure and manage a cloud deployment of DeepCell 2.0 on all commonly used operating systems. Using single-cell segmentation as a use case, we show that users with suitable training data can train models and analyze data with those models through a web interface. We demonstrate that by matching analysis tasks with their hardware requirements, we can efficiently use computational resources in the cloud and scale those resources to meet demand, significantly reducing the time necessary for large-scale image analysis. By reducing the barriers to entry, this work will empower life scientists to apply deep learning methods to their data. A persistent deployment is available at http://www.deepcell.org.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/505032DOIDiscussion Paper
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license. We thank numerous colleagues including Anima Anandkumar, Michael Angelo, Ian Brown, Andrea Butkovic, Long Cai, Markus Covert, Michael Elowitz, Jeremy Freeman, Christopher Frick, Lea Geontoro, KC Huang, Greg Johnson, Leeat Keren, Nora Koe, Takamasa Kudo, Daniel Kyme, Daniel Litovitz, Derek Macklin, Shivam Patel, Nicolas Pelaez Restrepo and Cole Pavelchek for helpful discussions and gratefully providing the data necessary to develop this work. We gratefully acknowledge support from the Allen Discovery Center at Stanford University, Google Research Cloud, Figure 8’s AI for everyone award, and a subaward from NIH U24CA224309-01. All data that was used to generate the figures in this paper are available at http://www.deepcell.org/data. A persistent deployment of the software described here can be accessed at http://www.deepcell.org. All source code is available at http://www.github.com/vanvalenlab. Detailed instructions are available at http://deepcell.readthedocs.io/.
Funders:
Funding AgencyGrant Number
Stanford UniversityUNSPECIFIED
Google Research CloudUNSPECIFIED
Figure 8UNSPECIFIED
NIHU24CA224309-01
Record Number:CaltechAUTHORS:20190102-092232849
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190102-092232849
Official Citation:DeepCell 2.0: Automated cloud deployment of deep learning models for large-scale cellular image analysis Dylan Bannon, Erick Moen, Enrico Borba, Andrew Ho, Isabella Camplisson, Brian Chang, Eric Osterman, William Graf, David Van Valen bioRxiv 505032; doi: https://doi.org/10.1101/505032
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:91967
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:03 Jan 2019 16:07
Last Modified:03 Jan 2019 16:07

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