Zegami has built a tool that could help speed up diagnosis of COVID-19, using a combination of deep learning and groundbreaking data visualisation. This platform has shown proficiency, using a limited dataset, in distinguishing between x-rays of COVID-19 infections and infections caused by viral or bacterial pneumonia, as well as images of healthy lungs.

Access a walkthrough of this cutting-edge case study to understand how machine learning and visualisation techniques can be used to speed up medical diagnosis and create better machine learning models.

Topics covered:

  • Sourcing data: understanding where and how to find and prepare training data
  • How to train a machine learning model: approach, pitfalls and considerations
  • Visualisation and verification of the model: applications and watch-outs

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About the Speakers
Doug Lawrence, Machine Learning Engineer

With a background in all things science, data and machine learning, Doug is always looking for unexpected and robust ways to solve problems. 

Steve Taylor, Founder & CSO

Steve’s personal mission is to disrupt the analysis of complex data to improve the world. He wants to put powerful but easy-to-use analysis methods into the hands of everyone – not just computer experts – by using innovative visualisation and interaction technologies.