Accelerate Histology AI Model Development With Encord

Increase micro and macroscopic data annotation speeds, create datasets with rich labeling protocols and deliver better training data to your histology and pathology AI models.

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How it works

Create Better Pathology and Histology Machine Learning Models With Encord

Encord allows you to annotate digital slides to create datasets for histology AI models, supervise the output and quality of your annotation teams and manage the expert review process.

Encord offers full support for all staining protocols (including hematoxylin and eosin stain (H&E), KI67 and HER2), keypoint and polygon annotation, a full label audit trail and the ability to create highly complex labeling protocols.

Histology pathology annotation

Efficient Histology Image Labeling

Encord is designed to help your skilled histology annotators (whether PhD students, in-house or a hired workforce) improve their labeling efficiency.

Encord makes it quicker and easier to count cells with keypoint annotation and use this to create microscopy training data. And Encord’s polygon labeling tool allows your annotators to create macroscopic regional annotations for creating training data for a range of use cases, such as oncology diagnosis.

Example of labeling cells in histology

A Single Tool for Viewing and Labeling Digital Slides

Encord gives you a single platform for viewing and annotating your digital slides. It reduces the need to book out expensive microscopy workstation time, as you can upload your digital slides and review for research from anywhere, anytime, directly in the Encord platform.

And then when you’re ready to start the annotation process, your digital slides are already in the Encord platform, saving time and creating a more efficient labeling workflow.

Segmentation of brain in CT scan

Powerful Labeling Protocols for Histology and Pathology

Encord supports rich labeling protocols within its microscopy and macroscopy image annotation tool.

You can build complex labeling protocols for as many feature types as you need to deliver the right histology training data to your machine learning teams.

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
Expert review quality control for histology and pathology

More Efficient Clinical Data Operations

Make your annotator management workflows more efficient with role-based access control, worklist support and configurable expert review pipelines.

Ensure your expert pathologists and histologists’ time is spent only looking at those images and labels that need their input.

Encord improves the quality of your histology training data

Deliver Better Histology Training Data

Encord supports clinical operations teams to identify dataset issues, biases and imbalances (and even monitor the performance of individual annotators).

Clinical operations can provide better training data to machine learning teams and reduce the time it takes to create experiments for histology and pathology datasets.

Encord supports FDA clearance and CE marking processes

Supporting Your Regulatory Journey

Encord has been designed to make it easier for you to meet your regulatory submissions, including FDA and CE approval.

As well as being HIPAA and SOC2compliant, Encord’s data pipelines, fully auditable labels and quality control and assurance features make regulatory compliance much less of a headache.

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