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
SEA TECHNOLOGY MAGAZINE Great white sharks enjoy what can only be described as a complex relationship with humankind. On one
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
Our new Colour by Column feature is a powerful new dimension to visual data exploration in Zegami, enabling richer views, which reveal more of the underlying patterns in your data.
Zegami has been appointed by Oxford University researchers at the Oxford Cardiovascular Clinical Research Facility to accelerate research into cardiovascular disease.
Zegami has been collaborating with MRC Weatherall Institute for Molecular Medicines to help clean its data and assist with the training of its machine learning models, specifically around developing a better understanding of which proteins in genes bind, and where they do this in the genome.