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AI enabled image analysis platform for scientific & medical imaging
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Why Zegami?
Zegami is unique because it easily works with both structured & unstructured data, from multiple sources: from images to documents and APIs to video. Based on Oxford University research, Zegami is being used by academics, researchers and commercial leaders across the world:
Researchers, scientists and engineers: The Plant Accelerator project at the University of Adelaide solves food supply issues in large automated greenhouses in Europe, Australia and the US, by giving researchers the ability to view thousands of images all at once on a screen, enabling them to spot patterns or anomalies in physiological and chemical traits.
Data scientists training machine learning models: Cancer Research UK funded project, developing a cancer detection system which identifies cancerous lesions in real-time by highlighting them on the video as the patient is being examined.
Data integration across silos: Companies such as Siemens, Honeywell and numerous other firms use Zegami to extract insights and answer questions from disparate data sources. This makes the process of data management and data cleaning faster, more efficient and gives them a competitive advantage.
High throughput image analysis
Explore large image datasets and video to unlock powerful insights from previously unutilised data sources. Drive innovation for scientific research and medical imaging.
Streamlined image annotation for AI
Deliver more accurate and robust machine learning models faster. Automate your data analysis with machine learning models specifically targeted to meet your needs.
Data integration across silos
Cleverly use all the data in your organisation to extract insights and answer questions from disparate data sources.
Case Studies
Latest demo collections
COVID-19
COVID-19 lung X-ray insights. Cutting edge AI and visualisation technology can speed up efforts by researchers to develop new tools for diagnosis.
Cancer Diagnosis
Oesophageal cancer video frames to find cancer lesions to train the machine learning model.
Predicting Heart Disease
This collection shows an example of using unsupervised machine learning to predict heart disease using images derived from echocardiogram data
High throughput screening
High throughput screening in Zegami as published in the Nature Communications article ‘Suppression of the alternative lengthening of telomere pathway
Our Clients
“The theory out there is that losing an employee is about the cost of two times their salary to be able to get someone to replace them and ramped up. By being able to retain more people which Zegami has helped us do, we probably saved millions of dollars by that alone.”
Chris Wollerman
CEO
“Without zegami I wouldn’t be able to feasibly accomplish my project in a reasonable amount of time. It just allows me to go through large amount of information in batches rather than individually and it is a complete game changer.”
Lance Hentges
Researcher
Our Partners
Latest News & Events
Stronger Together interview with Microsoft
In this interview with David Smith, Developer Advocate at Microsoft, our CEO Roger Noble explains how Zegami collaborated with the
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Roger Noble and Steve Taylor interviewed by Oxford Investment Consultants
Join OIC Principal and CFO Tania Wilson interview our CEO Roger Noble and CSO Steve Taylor.
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Data firm Zegami joins project to find cure for ME
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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We’d love to understand the nature of your business or application, what data you have (both structured and unstructured), and help you visualise what’s possible with it.
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