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.
Create a segmentation mask of target item (eg. Brain with tumour in MRI) through the use of image-to-image translation models.
Generate synthetic abnormal target items (eg. brain with tumor) from the mask and introduce variability by adjusting those labels.
Seamlessly integrate labelled data into your current framework through the use of an API.
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:
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.
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.
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.