Stay Informed: Transforming Radiology with Structured Reporting and Data-Driven Approaches

Dive into our activities, projects, and product updates. Catch on the latest industry news and learn who we are as a company and as a team.

Radiologist using for medical image analysis

Advancing Real-World Federated Learning in Radiology

Federated Learning (FL) enables collaborative model training without data centralization – a crucial aspect for radiological image analysis where privacy regulations would otherwise hinder the use of centralized data lakes. Despite its promise, however, FL has largely been confined to simulated environments.

This study aims to bridge the gap between simulated and real-world FL research by developing an FL infrastructure within the German Radiological Cooperative Network (RACOON), a project by the Netzwerk Universitätsmedizin (NUM).

Using mint Lesion™ to process radiological images, the study’s results show FL outperforms these methods, underscoring its value in practical applications. The study also provides a guide for establishing FL initiatives, highlighting strategic organization and robust data management to aid future researchers in implementing FL in clinical settings.

Read more about the study here.

Related Resources

Related Resources

Screenshot of the Mint Medical Template Designer interface showcasing customizable research templates and logic rule integration for efficient data collection.

Transform Your Research with the Template Designer

At Mint Medical, we know that organized and high-quality data is the foundation of successful research. That's why we've developed the Template…

ESOI-EORTC Workshop: Hands-On Training in Assessing Tumor Response to Treatment

The ESOI-EORTC workshop, hosted by the European Society of Oncological Imaging (ESOI) and the European Organization for Research and Treatment of…

Systems on FHIR: Driving Healthcare Innovation Through Interoperability

Interoperability is revolutionizing healthcare by enabling the seamless exchange of patient data across systems. This efficient data flow is critical…