Technological developments in the field of digital pathology have made it easier to store, view and organise medical images. It’s also easier to engage and collaborate with other professionals, as well as quickly seek second opinions from other clinicians.
Hear from our Chief Scientific Officer, Steve Taylor, as he talks at the Medical Imaging Convention Virtual event about the use of artificial intelligence and medical imaging to understand disease.
Explainable AI (XAI), is essential in allowing us to understand, communicate and adapt how machine learning models reach their decisions. Part two of our blog explores some of the benefits of XAI and how it can be used by, and benefit, healthcare professionals.
In a world that relies heavily on information technology, the importance of AI that we can understand, interpret and trust is becoming a necessity. In the first of our two-part blog series, we explore the evolution of AI and the increasing need for explainable AI.
More than a third renting in the 12 months will be first timers Zegami joint venture with Ebbon Group pioneers
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.