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

• Published in: Nature communications Vol. 10; no. 1; pp. 4551 - 7
• Main Authors: Godec, Primož, Pančur, Matjaž, Ilenič, Nejc, Čopar, Andrej, Stražar, Martin, Erjavec, Aleš, Pretnar, Ajda, Demšar, Janez, Starič, Anže, Toplak, Marko, Žagar, Lan, Hartman, Jan, Wang, Hamilton, Bellazzi, Riccardo, Petrovič, Uroš, Garagna, Silvia, Zuccotti, Maurizio, Park, Dongsu, Shaulsky, Gad, Zupan, Blaž
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07.10.2019; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/1564; 631/114/2164; Analytics; Animals; Artificial intelligence; Bone healing; Clustering; Computational Biology - methods; Computer applications; Data analysis; Data processing; Depth profiling; Dictyostelium - cytology; Dictyostelium - growth & development; Dictyostelium - metabolism; Embedding; Gametocytes; Green Fluorescent Proteins - genetics; Green Fluorescent Proteins - metabolism; Humanities and Social Sciences; Image analysis; Image classification; Image processing; Image Processing, Computer-Assisted - methods; Internet; Learning algorithms; Life Cycle Stages; Localization; Machine Learning; Medical imaging; Mice, Transgenic; Morphology; multidisciplinary; Neural Networks, Computer; Oocytes; Oocytes - metabolism; Osteoprogenitor cells; Progenitor cells; Reproducibility of Results; Saccharomyces cerevisiae - metabolism; Saccharomyces cerevisiae Proteins - metabolism; Science; Science (multidisciplinary); Scientific visualization; Visualization; Yeast; Yeasts
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-019-12397-x

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. Deep learning approaches for image preprocessing and analysis offer important advantages, but these are rarely incorporated into user-friendly software. Here the authors present an easy-to-use visual programming toolbox integrating deep-learning and interactive data visualization for image analysis.