A Workflow to Save Time
Genedata has developed an innovative high content screening (HCS) image analysis workflow based on deep learning that cuts image analysis times by an order of magnitude, while increasing data quality and reproducibility of results.
- Automates time consuming and repetitive tasks during image analysis set-up
- Increases reproducibility and detects complex phenotypes by eliminating the biased selection of handcrafted features
- Saves time by quickly being re-applied in different experimental settings
The pharmaceutical industry stands before a paradigm shift in HCS image analysis. Several recent publications have shown successful application of deep networks to the analysis of HCS images, where these revolutionary machine learning approaches outperform conventional approaches for feature extraction and phenotype classification by a wide margin. Beyond the improved quality of the results obtained, we have seen strong evidence that these approaches will enable accurate quantification of phenotypes that are difficult or impossible to measure with conventional approaches. They will also open the field to non-experts by removing barriers in the application of image analysis. Over the next 5 years, we expect Deep Learning to become the technology of choice for novel biological screening models.
Genedata partners with leading pharmaceutical companies to create the first commercially available solution for deep learning-based HCS image analysis. The new software system should enable significant scale-up of HCS operations and allow decommission of existing software tools that can only be used by experts. Interested parties should contact us to find out more about the new solution and how we can collaborate. Currently we offer the following collaboration models: