RYVER provides diverse synthetic medical images with pixel-level annotations to reduce bias in radiology AI training datasets. This technology enables AI developers to generate high-quality data in minutes, achieving cost savings of 80-90% compared to traditional data acquisition methods.
Funding
$1.4M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Generating diverse and accurately annotated medical imaging datasets for training radiology AI models is a time-consuming and expensive process. Traditional data acquisition methods struggle to represent the full spectrum of patient demographics and disease variations, leading to biased AI algorithms.
Solution
RYVER provides a platform for generating synthetic medical images with pixel-level annotations, enabling AI developers to create diverse and representative training datasets. The platform leverages pre-trained generative models that can be fine-tuned with proprietary data without compromising data security or privacy. By automating data creation and annotation, RYVER reduces the time and cost associated with traditional data acquisition, allowing AI teams to focus on model development and iteration. The platform also offers tools to assess the quality and utility of synthetic data, ensuring that it improves the performance of diagnostic models.
Target Audience
The primary target audience includes medical AI teams, radiology researchers, and healthcare organizations developing and deploying AI-powered diagnostic tools.
Features
- Pre-trained generative models for various radiology modalities
- Python libraries for seamless integration into existing data pipelines
- Tools for assessing the quality and utility of synthetic data, including embeddings and downstream performance evaluation
- Graphical interface for discovering and managing synthetic data resources
- Automatic documentation of data usage for compliance purposes
- Ability to augment proprietary data and oversample underrepresented subgroups
- Fine-tuning capabilities to adapt generative models to specific datasets