Gradient Health, a medical technology company building an open research platform for medical imaging, has selected Paperspace as its MLOps platform to bring reproducibility and determinism to its machine learning pipeline.

Lung segmentations image set made available by Gradient Health

Gradient Health, founded by Duke University students and graduates, uses the latest machine learning technologies to build critical infrastructure for developers of imaging algorithms. These developers bring cutting-edge scientific research into the real world, building novel imaging workflows to the field of radiology for the benefit of physicians and scientists.

Misha Kutsovsky, Senior Machine Learning Architect at Paperspace, said: “Gradient Health is on a mission to help the medical imaging community innovate with the latest and greatest ML techniques. We’re excited to support their machine learning efforts and collaborate with them on this critical work.”

With Paperspace’s MLOps platform (which coincidentally shares the name Gradient), Gradient Health supports an interoperable and secure clinical environment for its developers. Having a tight machine learning feedback loop is key to delivering a valuable product.

“Through our partnership with PACS vendors, our goal is to provide all researchers secure access to train and validate models on production-scenario medical datasets. Paperspace provides us a way to better interface with researchers; allowing us to focus on building tooling, and models that are widely applicable to the field.”

Ouwen Huang, Gradient Health CEO

Working together, Gradient Health and Paperspace will make it easier than ever to translate leading scientific research into real-world machine learning algorithms that improve outcomes for patients.

To learn more about Gradient Health, please visit: www.gradienthealth.ai