Microscopes are capable of producing vast amounts of data, and when used in automated laboratories both the number and size of images present many challenges for storing, categorizing, analyzing, annotating, and transforming the data into actionable information that can used for decision making; either by humans or machines. In this presentation I will describe the informatics system we have established at the Department of Pharmaceutical Biosciences at Uppsala University, which consists of computational hardware (CPUs, GPUs, storage), middleware (Kubernetes), imaging database (OMERO), and workflow system (Pachyderm) to perform online prioritization of new data, as well as the continuous analytics system to automate the process from captured images to continuously updated and deployed AI models. The AI methodologies include Deep Learning models trained on image data, and conventional machine learning models trained on features extracted from images or chemical structures. Due to the microservice architecture the system is scalable and can be expanded using hybrid-architectures with cloud computing resources. The informatics system serves a robotized cell profiling setup with incubators, liquid handling and high-content microscopy. The lab is quite young and is targeting applications primarily in drug screening and toxicity assessment, with the aim to improve research using AI and intelligent design of experiments.
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