One of the most crucial developments in AI – and one which has enjoyed exponential growth recently – is its application for the advancement of medical research and predictive analytics. When applied to the analysis of large and complex data sets – for example databases comprising hundreds of thousands of medical images from multiple sources and locations – AI is a very useful assistive tool, supporting with triage and diagnoses that would otherwise require considerable time and expertise from highly experienced clinicians.
Zegami is a great tool for managing, sharing and getting insights from your data in an intuitive way that can
We’re currently working on a new capability for Zegami which adds a new dimension to image analysis, and we’re really excited to be able
We’ve added the ability to create Zegami collections with only images. It’s never been easier to create a Zegami collection and should take only minutes to start gaining insights into a set of image files.
We are excited to introduce our new feature – Automated image and data clustering! Besides offering a fascinating representation of the data, it provides some immediate practical benefits such as efficient labelling and rapid outlier detection.
In this interview with David Smith, Developer Advocate at Microsoft, our CEO Roger Noble explains how Zegami collaborated with the University of Oxford to analyse a research database of lung X-rays, and develop a machine learning model to identify cases of COVID-19.
Join OIC Principal and CFO Tania Wilson interview our CEO Roger Noble and CSO Steve Taylor.
Zegami, the Oxford University data visualisation spin-out, has joined an international team of medical researchers to try and find the
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FINANCIAL TIMES Zegami, a data visualisation spinout from Oxford University, has developed a machine learning tool that aims to diagnose
13th May, 11:00-11:40 BST Zegami has built a tool that could help speed up diagnosis of COVID-19, using a combination
Zegami has developed a new machine learning model using x-rays of Covid-19 infected lungs, artificial intelligence techniques and data visualisation tools that could help medical professionals identify corona virus cases more effectively