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Revolutionizing Diagnosis and Treatment with Medical Imaging AI

AI-enabled imaging, diagnostics and workflow support previously thought impossible
Apheris is transforming medical imaging with federated learning while ensuring patient data privacy and compliance. We enable healthcare organizations and medical imaging AI teams to jointly develop AI diagnostics while protecting sensitive data. Fostering accurate, efficient diagnostics and paving the way for early disease detection and personalized medicine.

The gateway to federated learning

Proprietary medical imaging is spread across multiple sites and often in silos. Used together with publicly accessible data, organizations can advance medical imaging AI in ways not previously possible. Apheris computational governance solution enables secure federated learning, while respecting data privacy and compliance obligations for each site.

Federated

Imaging data stays where it resides under the full control of the data custodian, who determines how the data can be used

Private & secure

Apheris offers state-of-the art security, privacy, and governance to ensure compliance with regulatory requirements while data and model IP stay protected

Integrated & scalable

AI teams can build on existing model and data pipelines and leverage Apheris capability to scale to thousands of deployments or very large compute workloads.

Built with security & privacy at the core

Federated learning

Access medical imaging data across multiple sites, public and private institutions. Train weakly supervised AI models to assist with segmentation, classification, or diagnosis. Models learn from distributed imaging data of multiple organizations for more accurate, generalizable results.

Computational governance

Let your imaging data providers stay in control of their data and reduce the costs associated with sharing or centralizing data. Custodians define permitted computations for each dataset and automatically validate incoming requests against configured asset policies.

Private, secure federated data networks

Protect patient privacy and ensure compliance while still allowing for large-scale access to data of multiple sites across organizational and geographical boundaries. Start ground-breaking collaborations while protecting IP and data privacy.

AI is revolutionizing medical imaging

Using proprietary data of your customers to customize models is opening up new opportunities. Continuously improve algorithms, create new capabilities, and deliver them to customers faster than ever.

Scalable, cross border AI

Leverage existing model and data pipelines, while protecting model IP, and scale out to large workloads, including across organizational and geographical borders. Utilize data from across customer sites to unlock the true potential of medical imaging data for AI.

What can medical imaging AI do with computational access to sensitive data

Enhanced diagnostic accuracy

Early disease detection

Personalized treatment

Private, secure, governed ML

Want to learn more about how Apheris can help you with safe algoritmic access to sensitive patient data?

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Guide

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:

checkmark Benefits of federated data ecosystems in pharma and healthcare
checkmark Examples in drug discovery, clinical trials and commercial growth
checkmark How data collaboration compares with data sharing
checkmark How to get started with federated data ecosystems
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.

In this case study you will learn:

checkmark How two pharma companies use protein-ligand interaction data and AI models to predict target binding of novel compounds
checkmark How a pharma company fueld their drug discovery activities by leveraging molecule data from their suppliers
checkmark How an AI-focussed health tech company developed AI models to improve decision-making processes for cancer treatments
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.

In this white paper, you will learn:

checkmark How you can preserve intellectual property while generating value from sensitive data
checkmark Deep dive into federated and secure QSAR models
checkmark How Apheris prevents uncontrolled data sharing
checkmark How you can pave the way to precision medicine with federated and secure QSAR model training
Guide

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:

checkmark Benefits of federated data ecosystems in pharma and healthcare
checkmark Examples in drug discovery, clinical trials and commercial growth
checkmark How data collaboration compares with data sharing
checkmark How to get started with federated data ecosystems