Genedata Imagence

Amazon Rekognition makes it easy to add image and video analysis to your applications using proven, highly scalable, deep learning technology that requires no machine learning expertise to use. With Amazon Rekognition, you can identify objects, people, text, scenes, and activities in images and videos, as well as detect any inappropriate content. Amazon Rekognition also provides highly accurate facial analysis and facial search capabilities that you can use to detect, analyze, and compare faces for a wide variety of user verification, people counting, and public safety use cases. With Amazon Rekognition Custom Labels, you can identify the objects and scenes in images that are specific to your business needs. For example, you can build a model to classify specific machine parts on your assembly line or to detect unhealthy plants. Amazon Rekognition Custom Labels takes care of the heavy lifting of model development for you, so no machine learning experience is required.

We integrate clinical and experimental data using machine learning to navigate human disease biology and advance precision medicines. Our patent-pending Contingent AI™ understands relationships within the data to provide sophisticated insights. We address data bias by iterating on machine learning models based upon decisions made in the pre-processing and feature engineering stages. We leverage zebrafish, cellular and other phenotypic animal models to validate in silico predictions in vivo experiments and genetically modify them in vitro and in vivo, to improve translation. Using active learning and computer vision on validated models for cardiac, central nervous system and rare disorders, we rapidly incorporate new data into our machine learning models.