Abstract
Supervised machine learning is a powerful and widely used method to analyze high-content screening data. Despite its accuracy, efficiency, and versatility, supervised machine learning has drawbacks, most notably its dependence on a priori knowledge of expected phenotypes and time-consuming classifier training. We provide a solution to these limitations with CellCognition Explorer, a generic novelty detection and deep learning framework. Application to several large-scale image data sets demonstrates that CellCognition Explorer enables discovery of rare phenotypes without user training, thus facilitating assay development for high-content screening.
Copyright
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