Democratized image analytics by visual programming through integration of deep models and small-scale machine learning

Primož Godec, Matjaž Pančur, Nejc Ilenič, Andrej Čopar, Martin Stražar, Aleš Erjavec, Ajda Pretnar, Janez Demšar, Anže Starič, Marko Toplak, Lan Žagar, Jan Hartman, Hamilton Wang, Riccardo Bellazzi, Uroš Petrovič, Silvia Garagna, Maurizio Zuccotti, Dongsu Park, Gad Shaulsky and Blaž Zupan ()
Additional contact information
Primož Godec: University of Ljubljana
Matjaž Pančur: University of Ljubljana
Nejc Ilenič: University of Ljubljana
Andrej Čopar: University of Ljubljana
Martin Stražar: University of Ljubljana
Aleš Erjavec: University of Ljubljana
Ajda Pretnar: University of Ljubljana
Janez Demšar: University of Ljubljana
Anže Starič: University of Ljubljana
Marko Toplak: University of Ljubljana
Lan Žagar: University of Ljubljana
Jan Hartman: University of Ljubljana
Hamilton Wang: Baylor College of Medicine
Riccardo Bellazzi: University of Pavia
Uroš Petrovič: University of Ljubljana
Silvia Garagna: University of Pavia
Maurizio Zuccotti: University of Pavia
Dongsu Park: Baylor College of Medicine
Gad Shaulsky: Baylor College of Medicine
Blaž Zupan: University of Ljubljana

Nature Communications, 2019, vol. 10, issue 1, 1-7

Abstract: Abstract Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we use the visual programming toolbox Orange ( http://orange.biolab.si ) to simplify image analysis by integrating deep-learning embedding, machine learning procedures, and data visualization. Orange supports the construction of data analysis workflows by assembling components for data preprocessing, visualization, and modeling. We equipped Orange with components that use pre-trained deep convolutional networks to profile images with vectors of features. These vectors are used in image clustering and classification in a framework that enables mining of image sets for both novel and experienced users. We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing, identification of developmental competence in mouse oocytes, subcellular protein localization in yeast, and developmental morphology of social amoebae.

Date: 2019
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DOI: 10.1038/s41467-019-12397-x

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