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.
Highlights
Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists
Feature learning with deep convolutional neural networks is implicit, and training the network usually focuses on particular tasks, such as breast cancer detection in mammography[2], subcellular protein localization[3], or plant disease detection[4]
Modern image analytics are well supported in programming environments, such as those built around Python and enhanced with libraries for deep learning such as TensorFlow, PyTorch, and Keras
Summary
Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Just like our visual cortex can adapt to the analysis of many scenes and images, a deep network pretrained on a sufficiently large number of diverse images may infer useful features from a broad range of new image sets. This idea is based on transfer learning[5], a machine-learning technique that stores the knowledge obtained from one problem in a trained model and applies it to another problem, which may be quite different. While the proposed framework is general and can consider any type or class of images, we focus here on biomedicine and demonstrate the utility of the tool for analysis of images from molecular and cell biology
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