V prestižni znanstveni reviji Nature Communications so raziskovalci Laboratorija za bioinformatiko UL FRI s sodelavci objavili članek "Democratized image analytics by visual programming through integration of deep models and small-scale machine learning". Čestitamo!

V članku predstavijo poenostavljen, odprt dostop do globokih modelov, ki omogočajo analizo fotografij. Pokazali so, kako je mogoče z ekstrakcijo značilk fotografije gručiti ali pa z njimi graditi napovedne modele. Uporabnost vektorskih vložitev v članku prikažejo na treh bioloških primerih, vendar pa se globoke modele da uporabiti tudi na zelo drugačnih domenah.

Povzetek (v angleščini):

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.

Članek v odprtem dostopu lahko preberete na:

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