Using state-of-the-art research to save millions in cost and months of time when implementing you machine learning model.

Machine Learning (ML) is revolutionising healthcare, by democratising expert-level analysis to better prevent, diagnose, and treat disease.

Most ML models deployed in the medical image domain are trained in a supervised setting. This has many challenges:

  1. Training supervised models requires datasets with vast amounts of training pairs, that neeed to be annotated by specialists. This is prohibitively expensive and time consuming when accounting for a pathologist’s salary and the team that is often required for cross-checking. A single image can take an…

Nerativ

GANs for Dataset Engineering. team@nerativ.io

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