Image Archive 


Abstract Image


High throughput image analysis of large image data sets: extract insights, answer specific questions and qualify images for further study.

Accelerate machine learning: use high throughput image annotation to build high quality training data sets, rapidly annotate and tag images to verify machine learning models.

Enrich image archives: browse huge datasets at high level in exquisite detail, find rare or lost images and spot trends. Reinstate missing metadata.

Discover insights from large and complex data sets: graph data and use graph images to spot trends  across multidimensional data sets.


  • Quality Control: investigate and analyse image datasets to spot outliers and mistagged images.
  • Collaboration: work on a data set remotely even if you have low bandwidth connection.
  • Publishing: the original image data available to anyone who has access as to the archive.
  • Machine learning: curate and build training data sets for training machine learning models.
  • Image tagging: annotate and tag images and video in preparation for training.
  • Quality Control: test and optimise effectiveness of machine learning models.
  • Labelling: label large data sets with Zegami’s unique high throughput tagging method to assist in data augmentation, where little or no data exist or where data is incorrect.
  • Quality Control: Bring order to unstructured data sets by viewing and amending metadata.
  • Organise data: structure data using automated ML techniques to easily spot patterns, missing and incorrect data.
  • Machine Learning: rapidly build training data sets and spot missing, incomplete or inaccurate data leading to higher quality ML models.
  • Quality Control: quickly find interesting outliers that can be missed with traditional automated methods by ‘augmenting user’s ability to see lots of information.


  • Quickly find insights and publish findings to commercialise research faster.
  • Real time visualisation and analysis.
  • Collaborate from anywhere with our cloud based access.
  • Higher project success rate, less waste on re-running experiments.
  • Speed up quality control of large data sets by finding outliers or problems in the data.
  • Rapidly build training data sets and spot missing, incomplete or inaccurate data leading to higher quality ML models.
  • Uncover hidden data biases.
  • Produce detailed segmentation masks to increase ML model accuracy.
  • Significantly reduce the time and resources devoted to building custom tooling, image processing systems and data pipelines.
  • Enhance existing data sets with dimensionality reduction / unsupervised machine learning views.
  • Easily spot missing, incomplete or inaccurate metadata.
  • Integrate custom ML models to extract features from images and make data sets more queryable.
  • Survey large datasets at high level to look for patterns, clusters and outliers with the ability to deep zoom into regions of interest.
  • Avoid using arbitrary cutoffs (e.g. scores, p-values) to see the patterns in the underlying data.

Supported Data Types




Images and metadata

Image Formats

dcm, tiff, dzi, jpg, gif, png (8 and 16 bit colour depth) and other formats upon request

Image Sources

Examples include: Infrared, Satellite, CT, MRI, X-Ray




Our flexible HTTP API allows straightforward integrations with other systems.
OAuth, 3rd Party Authentication, Single Sign On (coming soon)


Australian Plant Phenotyping FacilityHigh throughput plant phenotyping

WIMMHigh Throughput Screening (Co-localisation image analysis)

Illan Davis Lab

Understand how the nervous system works using super-resolution microscopy

Cardiovascular Clinical Research Facility (CCRF)

Retrospective studies of echocardiogram images

Leeds University/Hospital (Innovate UK grant)

Digital pathology to look at heterogeneity of staining across samples

Satellite Catapult

Natural disaster planning

Cancer Research UK

Real-time oesophageal cancer detection in endoscopy video

Formula One

Key brand identification

Satellite Applications Catapult

Natural disaster management

Leeds University/Hospital (Innovate UK grant)

Digital pathology

Leeson group

Echocardiogram archive

Edinburgh mouse atlas (CCRF)

Echocardiogram repository


X-Ray Analysis collaboration project

Nuffield Department of Women’s & Reproductive Health

Diagnosis of ME and MS patients from blood samples using Raman spectroscopy

Biomedical researchers working in omic technologies

Understanding complex signals from next generation sequencing such as ChIP-Seq and RNA-Seq


Biomarker discovery in echocardigram data