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Journal Articles Nature Methods Year : 2019

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

Abstract

Deep learning methods have shown extraordinary potential for analyzing very diverse biomedical data, but their dissemination beyond developers is hindered by important computational hurdles. We introduce ImJoy (https://imjoy.io/), a flexible and open-source browser-based platform designed to facilitate widespread reuse of deep learning solutions in biomedical research. We highlight ImJoy's main features and illustrate its functionalities with deep learning plugins for mobile and interactive image analysis and genomics. Deep learning methods, which use artificial neural networks to learn complex mappings between numerical data, have enabled recent breakthroughs in a wide range of biomedical data analysis tasks. Examples for imaging data include image segmentation 1,2 and medical diagnosis, where deep learning vastly outperforms more traditional methods and often exceeds human expert performance 3,4 , or methods to enhance microscopy images, e.g. for denoising or 1
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pasteur-02401584 , version 1 (10-12-2019)

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Attribution - NonCommercial - ShareAlike

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Wei Ouyang, Florian Mueller, Martin Hjelmare, Emma Lundberg, Christophe Zimmer. ImJoy: an open-source computational platform for the deep learning era. Nature Methods, 2019, 16 (12), pp.1199-1200. ⟨10.1038/s41592-019-0627-0⟩. ⟨pasteur-02401584⟩
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