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

Povzetek

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.

Ključne besede

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

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FRI - Fakulteta za računalništvo in informatiko
UDK: 004.9:577
COBISS: 32755751 Povezava se bo odprla v novem oknu
ISSN: 2041-1723
Št. ogledov: 475
Št. prenosov: 199
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Vrsta dela (COBISS): Članek v reviji
Strani: str. 4551-1-4551-7
Letnik: ǂVol. ǂ10
Čas izdaje: 2019
DOI: 10.1038/s41467-019-12397-x
ID: 12688985