Revolution

Labelled, Customizable & Infinite Medical Images

Copy of Michael - Arist
Michael - Arist
Never Have to Worry About Annotation Again

Yuva AI's platform uses a multi-tiered General Adversarial Network (GAN) to generate labelled medical imaging datasets (like X-Rays, CT-Scans) to help you overcome the largest bottleneck in developing machine learning models.

How Does it Work?
.01
Create Label Map

Create a segmentation mask of target item (eg. Brain with tumour in MRI) through the use of image-to-image translation models.

Michael - Arist (1)
Michael - Arist (2)
.02

Generate

Generate synthetic abnormal target items (eg. brain with tumor) from the mask and introduce variability by adjusting those labels.

.03

Export

Seamlessly integrate labelled data into your current framework through the use of an API.

Michael - Arist (3)
Better Data. Less Time. More Cost-Effective.

Here's why Yuva AI's industry-leading platform enables you to overcome the key barriers to obtaining training data for your healthcare AI applications: 

.01

Security & Privacy

Unlike real data that has simply been stripped of personal identifiers, re-identification is never possible with synthetic data, guaranteeing privacy and compliance with laws like CCPA and GDPR.

Group-195.svg
Group-196.svg
.02

Edge Cases = Value

Your key objective is to identify pathological cases such as cancer. However, there is comparatively less data available on these target cases. Synthetic data enables the production of very valuable imagery whereas traditional labelling cannot. 

Group-197.svg
.03

Lack of Resources

There simply aren't enough medical professionals or medical students that can be trained to annotate data accurately for a large dataset. An automated approach is required to achieve the dataset size required to build an accurate model. 

Be the first to get access. Sign up for updates here: