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ImJoy: an open-source computational platform for the deep learning era

Nature Methods volume 16, pages 1199–1200 (2019)Cite this article

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To the Editor — Deep learning (DL) methods achieve breakthrough performances in analyzing biomedical data across countless tasks, including medical diagnostics, DNA sequence analysis, augmented microscopy and drug design. Combined with increasing data repositories in genomics, imaging and other fields, such successes underlay a growing demand to adapt DL methods to new datasets and questions1. However, the dissemination of DL approaches faces considerable hurdles. Most published DL studies2,3,4,5 require users to retrain models on their own data to obtain the best performance and/or avoid erroneous results. Although trained models are frequently available through web applications or ImageJ plugins, retraining is typically only possible via scripts or command lines, rather than graphical user interfaces (GUIs). In addition, the complexities of setting up the required hardware and software environments often constitute forbidding obstacles6. Furthermore, the large datasets and computational resources typical of current DL successes pose challenges to traditional desktop-oriented software that tightly couple GUI and computation. Cloud services can partly alleviate these difficulties, but raise privacy and confidentiality issues that can be prohibitive for medical data7. Meanwhile, deploying scientific software to mobile platforms can make them accessible to billions of people8, enabling large-scale biomedical research and citizen science. These opportunities and challenges call for new computational frameworks.

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Fig. 1: Overview of ImJoy.

Code Availability

Source code for ImJoy and the example plugins is available at https://imjoy.io and https://github.com/imjoy-team/example-plugins, respectively.

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Acknowledgements

This work was funded by the Institut Pasteur. W.O. was a scholar in the Pasteur–Paris University (PPU) International PhD program and was partly funded by a Fondation de la Recherche Médicale (FRM) grant to C.Z. (DEQ 20150331762). W.O. is a postdoctoral researcher supported by the Knut and Alice Wallenberg Foundation (2016.0204) and Erling-Persson Foundation (20180316) grants to E.L. We also acknowledge Investissement d’Avenir grant ANR-16-CONV-0005 for funding a GPU farm used for testing ImJoy. We thank the IT department of Institut Pasteur, in particular S. Fournier and T. Menard, for providing access to the kubernetes cluster and DGX-1 server for running and testing the ImJoy plugin engine and for technical support. We thank Q.T. Huynh for maintaining the GPU farm and for advice and assistance during the development of ImJoy. We also thank A. Martinez Casals, P. Thul, H. Xu, A. Aristov, A. Cesnik, C. Gnann, J. Parmar, K.M. Douglass, N. Stuurman, X. Hao, S. Dai, A. Hu, D. Guo, K. Zhou for testing and helping with ImJoy plugin development. We thank E. Rensen for proofreading the manuscript. We thank J. Nunez-Iglesias, S. Mehta, B. Chhun, J. Batson, L. Royer, N. Sofroniew and M. Woringer for useful advice and discussion.

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Authors and Affiliations

  1. Imaging and Modeling Unit, Institut Pasteur, UMR 3691 CNRS, C3BI USR 3756 IP CNRS, Paris, France

    Wei Ouyang, Florian Mueller & Christophe Zimmer

  2. Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden

    Wei Ouyang, Martin Hjelmare & Emma Lundberg

  3. Department of Genetics, Stanford University, Stanford, CA, USA

    Emma Lundberg

  4. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Emma Lundberg

Authors
  1. Wei Ouyang
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  2. Florian Mueller
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  3. Martin Hjelmare
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  4. Emma Lundberg
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  5. Christophe Zimmer
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Corresponding authors

Correspondence to Wei Ouyang or Florian Mueller.

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Supplementary Information

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Ouyang, W., Mueller, F., Hjelmare, M. et al. ImJoy: an open-source computational platform for the deep learning era. Nat Methods 16, 1199–1200 (2019). https://doi.org/10.1038/s41592-019-0627-0

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