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

Godec, Primož (author), Pančur, Matjaž (author), Ilenič, Nejc (author), Čopar, Andrej (author), Stražar, Martin (author), Erjavec, Aleš (author), Pretnar, Ajda (author), Demšar, Janez (author), Starič, Anže (author), Toplak, Marko (author), Žagar, Lan (author), Hartman, Jan (author), Hamilton, Wang (author), Bellazzi, Riccardo (author), Petrovič, Uroš (author), Garagna, Silvia (author), Zuccotti, Maurizio (author), Park, Dongsu (author), Shaulsky, Gad (author), Zupan, Blaž (author)

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.

• Language: English
• Keywords: algorithm, biochemical composition, data assimilation, data mining, image analysis, machine learning, numerical model, protein, visualization
• Work type: Article (dk_c)
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of computer and information science ; BF - Biotechnical Faculty
• Year: 2019
• Publication status in journal: Published
• Article version: Publisher's version of article
• Number of pages: 7 str.
• Numbering: Vol. 10, art. 4551
• UDC: 004.9:577
• ISSN on article: 2041-1723
• DOI: 10.1038/s41467-019-12397-x
• COBISS.SI-ID: 32755751
• Publication date in RUL: 25.03.2021

Record is a part of a journal

• Title: Nature communications
• Publisher: Springer Nature
• ISSN: 2041-1723
• COBISS.SI-ID: 2315876

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 25.03.2021