Purpose-built software tools to accelerate computer vision in healthcare

Encord empowers leading medical institutions and companies to address some of the hardest challenges in computer vision for healthcare.

Use cases

Transforming medical imaging for better patient outcomes

Computer vision is revolutionising progress in medical imaging - from diagnostics and pathology to robotic surgery.


AI for precision gastroenterology.

  • Track polyps in video
  • Colonography and scope videos in one tool
  • Support Mayo and UCEIS scoring rubrics


Annotate modalities such as CT, MRI, and X-ray.

  • High-precision annotation of voxels
  • Hanging protocols
  • Axial, coronal, and sagittal view


Handle high-resolution microscopy images with ease.

  • Image processing and filtering options
  • Automated measuring capabilities
  • Tools for fine-grained segmentations


Annotate ultrasound data with high accuracy.

  • Expert review workflows
  • Object detection, classifications and segmentations
  • Handle 2D and 3D


Label and classify a variety of conditions.

  • Flexible label taxonomies
  • Pixel perfect segmentation of lesions
  • Custom review processes keep experts in the loop

Surgical devices

Accurately track devices in surgical videos.

  • Native video capabilities for streamlined annotation
  • Automation features reduce manual burdens
  • 100% data-secure for privacy guarantees
Stanford Medicine
Stanford Medicine

The Division of Nephrology reduced experiment duration by 80% while processing 3x more images.

Stanford was using three different pieces of software to identify, annotate, and count podocytes and glomeruli in microscopy images.
Stanford started using Encord's annotation tools & SDK to automate segmentations, count, and calculate sizes of segments.
With Encord, Stanford researchers reduced experiment duration from an average of 21 to 4 days while processing 3x the number of images.
Reduction in experiment duration
Number of images
1 platform
... and not 3

How it works

Emphasizing medical data quality for healthcare

Expert review workflows, fully auditable, automated data quality checks. Fully compliant medical-specific tooling for medical imaging annotation.

Specialised tools

Annotate medical imaging data such as microscopy images, ultrasound videos, and volumetric DICOM images, with one platform.

Expert review

Define custom expert review workflows to make the most of your domain experts.

Automated quality assessment

Combine human specialists with automated intelligence to ensure the highest quality standard for your data and labels.

Automated medical video and imaging labeling

Use our object tracking & interpolation features to reduce costs. Use micro-models to accelerate your active learning workflows and get to production faster.

Compliant & secure

Capture maximal efficiency gains of our automation features while retaining 100% control of your data.


Ensure better, faster, and cheaper approval processes. Closely monitor annotator throughput and quality.
DICOM Cropped

Native medical imaging capabilities

We support all common medical image modalities including ultrasound, gastro videos, and offer a leading solution for volumetric DICOM images.


Granular quality control

From satisfying regulatory and compliance requirements to offering customisable annotation review processes, our software is developed alongside, and designed for use by medical experts.

KCL Logo
King's College London

KCL used Encord to achieve a 6.4x average increase in labeling efficiency for GI videos.

Using clinicians to annotate pre-cancerous polyp videos had prohibitively high costs to produce large datasets.
Deployed Encord's micro-model module to increase clinician labeling efficiency and automate 97% of produced labels.
Highest expense clinician saw 16x labeling efficiency improvement. Cut model development time from 1 year to 2 months.
Faster than manual labeling
Automated labels
Faster to AI in production
MSK logo
Memorial Sloan Kettering Cancer Center

MSK adopted Encord to build custom label protocols for pulmonary thrombosis projects.

Detecting and classifying vena cava filters in complex label protocols (ontologies) rendered existing & open-source tools unusable.
Deployed Encord's label protocol studio to build custom protocols, DICOM annotation tool, worklists & automation modules to increase efficiency.
Project made feasible by the flexibility offered by Encord's ontology study.
Protocol configurations
10 minutes
Total setup time

AI-accelerated medical image segmentation