Genedata Imagence® lets you train a deep neural network to classify cellular phenotypes in HCS images for unbiased, high-quality results. It automates your analysis to put the power of deep learning algorithms in the hands of assay biologists.
Our ImageXpress Micro high-content imaging system customers can now easily implement walk-away, automated image analysis workflows, while gaining deep insights with Genedata’s Imagence and Screener software in much less time.
Deployed in the AstraZeneca cloud, Genedata Imagence allows us to democratize Artificial Intelligence at scale across our organization so more scientists can run these assays, extracting more and higher quality information, thereby increasing the quality of decision making on our projects.
The real impact was being able to apply deep learning to classify phenotypes from images, and being able to do it in a way that was part of sort of a “business-as-usual” workflow, as a more accessible technology for biologists.
Genedata Imagence empowers biologists to directly and immediately analyze HCS imaging data using sophisticated deep learning techniques without any specialized algorithmic expertise, enabling you to:
Entirely automate image analysis setup, eliminating time-consuming and repetitive parameter tuning steps and specialist-controlled scripting workflows.
Automate identification and classification of complex phenotypes.
Instantly access your results via seamless integration with Genedata Screener®; immediately obtain a list of top hits with detailed statistics and plots.
Clarify Complex Results.
Ensure Data Quality.
Don’t shroud your analysis under abstract lines of code. The Genedata Imagence intuitive interface allows you to easily QC and explore data every step of the way, enabling a better and deeper understanding by allowing you to:
Access original images at all stages of the workflow, for rapid QC and data exploration.
Compare images and adjust training datasets in a matter of clicks.
Eliminate human bias with a deep learning approach. Consistent application of the same networks to new screening batches ensures reproducible and reliable analysis.