This is particularly powerful in case expected phenotypes are very rare. The software is part of the CellCognition project, which provides additional software, e.g., the CeCog Analyzer ( Held et al., 2010) for high-throughput by batch-processing with computer cluster support.Īs an alternative to manual annotation of phenotype classes, novelty detection methods can be applied to detect outlier phenotypes. The novelty detection and deep learning methods of CellCognition Explorer have been described in ( Sommer et al., 2017).ĬellCognition Explorer has been optimized for efficient processing of medium-scale microscopy data. In addition, a separate Deep Learning Module program enables to calculate statistical features of cells by deep learning methods. The main CellCognition Explorer program provides an integrated solution for image processing, feature extraction, classification by supervised machine learning or novelty detection. CellCognition Explorer is released under the GPLv3 and runs on Mac OS X or Windows.
