13th May, 11:00-11:40 BST
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.
Join us for 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.
Register to learn tips on:
- 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
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’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.