The Zegami solution for your industry

Scientists, researchers and academics today must consume and process vast quantities of data. Doing so efficiently and with accuracy is an increasing challenge. Furthermore, much of this data needs to be presented to others, both for external scrutiny e.g. regulators, and as part of public engagement.

Zegami provides a new way to visualise and work with data in a way that allows for efficient, systematic assessment and processing of data. The visual interface also provides an ideal medium for telling the story of the research through graphical representations of the data.

Researchers applying artificial intelligence to their visual and numerical data also use Zegami to prepare and tag their training data sets to increase the accuracy of their machine learning models.




Life Sciences

Machine Learning Data Curation

AI Lifecycle Management

Visualising Data on Screen


Over recent years there has been exponential growth in medical focussed AI Algorithms. However, there are significant barriers to adoption including inadequate frameworks to ensure privacy, security, quality and accuracy of AI. 

Integrations with high resolution and complex image types such as DICOM has allowed Zegami to become a world leader in AI development in the Medical Research field.

Further reading…

Cellular Pathology

ME / Gene Expression


CCRF / Cardio Vascular

Life Sciences 

In scientific research a huge amount of image data is being produced, whether it be from automated high throughput systems or from researchers manually taking photos. 

Zegami enables the analysis, evaluation and curation of these data sets and enables rapid AI development driving innovation. 

Further reading…

Agricultural Science

Plant Phenotyping

Biological Science

Marine Biology

The Zeg AI Image Search

AI Lifecycle Management

AI is becoming increasingly critical to many business operations. However, 82% of data scientists time is spent on cleansing data and improving training data.

Zegami delivers more accurate and robust machine learning models faster by automating your data analysis with machine learning models specifically targeted to meet your needs. 

Further reading…

ML Model Development & Validation

Diagnostic Tool Regulation

Covid 19 verification study

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Blog Post


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When it comes to AI in healthcare, data bias can lead to significant, albeit unintentional, consequences. We’ve put together these 6 tips to help you better understand AI bias.

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Demo Collection

DICOM Mammograms

This mammogram dataset consists of 3486 DICOM (Digital Imaging and Communications in Medicine) images. It contains normal, benign, and malignant cases with verified pathology information.

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Case Study: Solving global food shortages using Automated Image Analysis

Research at the Australian Plant Phenomics Facility is leading to the development of new and improved crops, healthier food and

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