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