Imaging-based phenotypic screening of cell-based disease models has become an indispensable tool for modern drug discovery. Despite the growing adoption of high-content screening (HCS), analyzing the complex imaging data produced by these systems can take weeks and typically requires handson programming by data scientists.
Recent advances in deep learning have enabled the possibility of automating these analyses. In this work we present a framework to analyze multiple image datasets with minimal tuning or optimization.