Manage your large-scale medical annotation with high output and quality

A robust medical annotation platform to turn medical images into rich data. Teams across the globe use Robovision to annotate, manage and scale multiple projects with ease.

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Robovision's medical annotation platform

6 reasons why you should choose Robovision for scalable annotation

Robovision’s medical annotation platform is a web-based tool for collaborative annotation with artificial intelligence (AI) technology. Medical experts can develop high quality medical training datasets efficiently in a secure and intuitive environment, with high throughput, accurate prediction and a quality control workflow.  

Hassle-free Annotation

Securely access our web-based platform locally, via a network or on the cloud.

Multiple Data Options with Customisation

Support Digital Imaging and Communications in Medicine (DICOM) as well as 2D and 3D images.

Large-scale & Efficient Annotation

Easily collaborate amongst multiple annotators with concurrent labeling or semi-automate for a large dataset with predictive labeling.

Quality Control Workflow

Require 2 admins to review and approve annotated datasets. Outsource or collaborate externally while maintaining quality.

High Performance with an Intuitive Interface

Designed in an ergonomic way, the interface was made to be intuitive for doctors such as radiologists to efficiently label in a seamless process.

Seamless Integration

Easily upload and export annotated datasets to any DICOM and PACS systems.

Robovision's web-based secured medical annotation platform

Hassle-free annotation

Web-based accessibility

Our web-based annotation platform allows you to label your data anytime and anywhere. Why download an application when all you need is reliable Internet connection to access your labeling tool. 

Maximum data security

To boost our platform’s speed performance even more, we can place a server in your location of choice. The data never leaves the servers nor transferred to a local device. This guarantees top security of your patient data at all times. 

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Multiple Data Options with Customisation

2D & 3D medical imaging

Our platform supports the annotation of both 2D and 3D images, from 2D X-rays and microscopy images to 3D CT Scan and MRI data. Simply import your data and use our labeling tool to start building your training datasets. 

Customisable for different user needs

Depending on your image type, we can provide you with custom labeling tools necessary for your project. This way we ensure a smooth and ergonomic labeling process even for a non expert, as no technical skills are required. 

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Large-scale annotation with predictive labeling

Improve predictions with semi-automatic labeling

By adding our state-of-the-art AI technology into this labor intensive process, a model’s output can make an initial annotation of raw data in real time. Our platform enables a tight feedback loop where your review team can improve this data and feed the improved annotations back into the model to increase its prediction accuracy.

Reduce labour intensive annotation process

With predictive labeling, you can significantly speed up your annotation process. A preliminary AI model is used to pre-segment the images which decreases the annotation time per image significantly.Predictions are used to pre-label data, which in turns reduce the average time for labeling and diminish manual annotation. 

Robovision enables concurrent labelling of medical images

Efficient annotation with concurrent labeling

Achieve higher throughput

Our platform can help you increase the throughput of your labeling process. Because it is web-based, different annotators can work concurrently on the same dataset and even the same medical image. Medical experts can thus collaborate in real time. 

Outsource your annotation or collaborating externally

Built for outsourcing and external collaborating purposes between your team, and the external annotators, you can assign labellers and grant restricted access to a labeling-only environment.
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Quality control workflow for better output

Four-eyes principle

The quality of your annotations or training datasets is a key factor in creating any AI-based clinical application. Reaching a conform ground truth is therefore crucial to ensure accuracy. Robovision’s review flow was built on the four-eyes principle, where every image is at least reviewed by 2 different lead annotators. 

Improving the quality of ground truth

You can improve the quality of your ground truth and expand your workforce with this built-in capability. For instance, if you need to quickly create a large custom dataset, it is possible now to have a team of non-expert labellers, whose work can be reviewed by a medical professional easily on our platform. Our toolset will also be tailored to best suit your team’s requirements.

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High performance with an intuitive Interface

Smooth adaption with platform training

Ensuring your annotation performance is as important as delivering a great tool. We offer platform training to help labellers learn about all the features and how to use it in the most efficient way possible. 

Seamless integration

With an intuitive interface, our platform was designed for use on a daily basis. Without interrupting your workflow, you can also export training datasets and integrate them easily to any DICOM or PACS systems. 

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Why Robovision?

Get work done faster and better

A Central Hub for Collaboration

Manage your labeling development process in a central environment. Collaborate internally or externally with confidence.

User First

No more setup and configuration hurdles with a secured web-based platform, that can be customised to meet your needs.

Excellent Services & Support

Stress free throughout your projects as we provide user trainings and technical support whenever you encounter an issue.

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“The Robovision annotation platform was an essential part of our in European multicenter Imaging Covid 19 project, namely for making high-quality CT-image annotations with a large group of medical annotators. Robovision provided us with all necessary tools and perfectly understood our needs.”

Erik Ranschaert,Prof. Dr. Radiologist, Key Opinion Leader in AI for Radiology

Annotate at scale

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