1. ImJoy: an open-source computational platform for the deep learning era
- Author
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Wei Ouyang, Martin Hjelmare, Florian Mueller, Emma Lundberg, Christophe Zimmer, Imagerie et Modélisation - Imaging and Modeling, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), School of Engineering Sciences in Chemistry, Biotechnology and Health [Stockholm] (CBH), Royal Institute of Technology [Stockholm] (KTH ), Stanford University, Chan Zuckerberg BioHub [San Francisco, CA], 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., ANR-16-CONV-0005,INCEPTION,Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs(2016), Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], and Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)
- Subjects
FOS: Computer and information sciences ,Data Analysis ,Computer Science - Machine Learning ,Computer science ,Machine Learning (stat.ML) ,Reuse ,computer.software_genre ,Biochemistry ,Quantitative Biology - Quantitative Methods ,Machine Learning (cs.LG) ,World Wide Web ,03 medical and health sciences ,MESH: Data Analysis ,Deep Learning ,Biomedical data ,[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] ,Statistics - Machine Learning ,Humans ,Plug-in ,Molecular Biology ,Biological sciences ,Quantitative Methods (q-bio.QM) ,030304 developmental biology ,0303 health sciences ,MESH: Humans ,business.industry ,Deep learning ,Cell Biology ,MESH: Deep Learning ,Open source ,FOS: Biological sciences ,[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie ,Artificial intelligence ,business ,computer ,Biotechnology - Abstract
International audience; 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
- Published
- 2019