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

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.

Keywords

algorithm;biochemical composition;data assimilation;data mining;image analysis;machine learning;numerical model;protein;visualization;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL FRI - Faculty of Computer and Information Science
UDC: 004.9:577
COBISS: 32755751 Link will open in a new window
ISSN: 2041-1723
Views: 475
Downloads: 199
Average score: 0 (0 votes)
Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Type (COBISS): Article
Pages: str. 4551-1-4551-7
Volume: ǂVol. ǂ10
Chronology: 2019
DOI: 10.1038/s41467-019-12397-x
ID: 12688985