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

• DOI: 10.1038/s41467-019-12397-x
• Publication Date: 2019-10-07T10:04:19Z
• Authors: 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; Blaž Zupan
• Keywords: info:eu-repo/classification/udc/004.9:577; Oocyte; 570; Saccharomyces cerevisiae Proteins; Neural Networks; Image Processing; Science; Green Fluorescent Proteins; Reproducibility of Result; Mice, Transgenic; Saccharomyces cerevisiae; Green Fluorescent Protein; Transgenic; Article; Machine Learning; Mice; Computer; 03 medical and health sciences; Computer-Assisted; biochemical composition; image analysis; Image Processing, Computer-Assisted; Animals; Dictyostelium; data assimilation; visualization; Internet; Life Cycle Stages; 0303 health sciences; algorithm; Animal; Q; Computational Biology; Reproducibility of Results; data mining; Life Cycle Stage; 004; machine learning; Oocytes; Neural Networks, Computer; numerical model; protein; Saccharomyces cerevisiae Protein

AbstractAnalysis 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.