comprehensive AI Model for Medical Image Generation

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Tackling data bottlenecks
in medical AI with
Synthetic Data

This collaboration will provide the medical AI community with an unprecedented volume of diagnostic-grade images, ensuring that developers have access to more complete datasets.

Benefits

Faster Development

Generate additional data in minutes instead of months enabling your AI teams to focus on building and iterating quicker than ever.

Lower
Cost

Use RYVER generative models to augment your proprietary data and oversample underrepresented subgroups.

Enhanced Accuracy

Generate additional data in minutes instead of months enabling your AI teams to focus on building and iterating quicker than ever.

Improved Robustness

RYVER models add diversity by being pre-trained on rich medical data from multiple partners.

How it works

Together, the two companies aims to develop a comprehensive AI model that will generate synthetic data based on real-world scans to provide a higher quality and broader range of medical images for training AI models.

Ask us anything about Synthetic Data

EXTRAs

CASE STUDY

Synthetic Training Data Improves Lung Nodule Classification

This case study shows how synthetic 3D Lung CTs including nodules of different size and texture can be used to enhance a best-in-class classification model.

PREPRINT

Evaluating Utility of Memory Efficient Medical Image Generation

This study evaluates quality and effectiveness of synthetic data by testing its impact on downstream segmentation tasks. Read the results in our preprint.