AI in medical imaging

AI-enabled imaging, diagnostics and workflow support previously thought impossible

Safely access data across boundaries

MedTech companies and healthcare organizations need access to large sets of high-quality imaging and machine data to develop robust AI-enabled tools. Apheris enables organizations to access data that spans across geographical and organizational boundaries, while protecting the intellectual property rights of the data custodians.

AI in medical imaging

Access sensitive customer data to build AI-enabled applications

Get access to your customers’ medical imaging data (MRI, Radiology). Accelerate development of robust and generalizable diagnostics or clinical decision support applications.

Build federated data networks

Start groundbreaking data collaborations across private and public institutions. Jointly learn from distributed imaging data of multiple organizations while protecting IP and data privacy.

Federated learning for digital pathology

Train weakly supervised AI models to assist with segmentation, classification or diagnosis in digital pathology. Protect patient privacy and ensure compliance while still allowing for large-scale access to data of multiple sites across countries.

Why medical imaging AI teams choose Apheris

IP protection of data

Apheris federated infrastructure ensures that the imaging data always stays where it resides and under the full control of the data custodian.

Integrated SDK

AI teams of MedTech companies can leverage existing model and data pilelines while protecting any model IP.


The Apheris platform scales to thousands of deployments or very large compute workloads, a key requirement for launching across your customer base.


Apheris offers state-of-the art security, privacy, and governance to ensure compliance with regulatory requirements. Data stays where it resides and never moves across geographical borders.

Any data, any size, anywhere

Want to learn more about how Apheris can help you power your infrastructure with federated machine learning and analytics?
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E-book - Federated Data Ecosystems in Pharma & Healthcare

Breakthroughs in healthcare are faster and more reliable with federated data ecosystems. By processing patient data without risking its integrity, data collaboration is safer and more effective than data sharing. This e-book highlights real-world examples and explains how to implement a federated data ecosystem in pharma and healthcare.

Case Study

Case Study: Pharma

Learn how to leverage federated and sensitive data at scale and how you can collaborate with your partners to advance and evolve the practice of life sciences.

White Paper

Privacy-preserving Data Ecosystems in Support of Drug Discovery

In recent years, Deep Learning has gained considerable traction in many fields, but none more so than in the realm of drug discovery. Applications range from generating de novo hit-like molecules, predicting drug-disease associations and activity, toxicology estimation, to the analysis of medical images.