The Collaborative Training Data Platform for Healthcare AI

Empowering leading healthcare institutions around the world, Encord enables efficient annotation of large training datasets from modalities like CT, X-ray, Ultrasound, MRI, Gastroenterology videos and more in DICOM and NIfTI formats.

Trusted by pioneering AI teams

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Gastroenterology

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Radiology

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Microscopy

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Ultrasound

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Dermatology

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Surgical Devices

Seamless Collaboration With Custom Workflows

From expert QA to annotator management, create fully customized, automated ML pipelines to improve the efficiency and quality of your annotation workforce.

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Native Medical Imaging Capabilities

Support for all common medical image modalities including ultrasound, gastroenterology videos, mammography and a leading solution for volumetric DICOM images with worklist support and 3D visualisation.

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70%

Reduction in Annotation Time

“Encord made it very easy to centrally keep track of annotations, including who had made them and who had reviewed them. It also had this great interpolation tool which was especially useful for the projects we were working on.”

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Faster Video Annotation Time

“Before, using clinicians to annotate precancerous polyp videos had prohibitively high costs because of the large number of datasets needed to train the model.”

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Reduction in Experiment Duration

Stanford researchers decreased experiment duration from an average of 21 to 4 days, while processing 3x the images and consolidating the number of platforms used from 3 to one.

Efficiently Annotate Medical Imaging Data

Simplified labeling of medical imagery and video with a variety of tools to meet your annotation needs.

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Polyline Annotation

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Segmentation

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Frame Classifications

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Polygons

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Bitmask

Simplify Regulatory Requirements Like FDA and CE Approval

As well as being HIPAA and SOC2 compliant, Encord’s data pipelines, fully auditable labels, quality control and de-identification features simplify regulatory compliance.

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Expedite Model Production With Active Learning

Train, test, and deploy models in automated pipelines whilst ensuring your model is being trained on the most informative data points using Active Learning techniques.

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