Fraunhofer MEVIS goes FAIR

Activities at Fraunhofer MEVIS to support the sharing of data, algorithms and services according to the FAIR principle, i.e. Findable, Accessible, Interoperable, and Reusable.



NFDI4Health – the National Research Data Infrastructure for Personal Health Data – deals with data generated in clinical trials, epidemiological and public health studies. The collection and analysis of these data on health and disease status and important factors influencing it are an essential component for the development of new therapies, comprehensive care approaches and preventive measures in a modern health care system.


Grand Challenge

Grand Challenge is a platform for end-to-end development of machine learning solutions in biomedical imaging.  The software behind Grand Challenge is open source, largely written by the Diagnostic Image Analysis Group at the Radboud University Medical Center Nijmegen. Fraunhofer MEVIS is working on a German node of Grand Challenge.



The COVID-19 Pandemic Radiological Cooperative Network RACOON is a joint project of the radiology departments at all 36 German university hospitals. Experts at all sites segment and annotate a large number of lung CT scans in a structured and uniform manner. The resulting data is used to develop medical assistance systems and an early warning system based on AI.



To allow the fast development of AI in pathology, the BIGPICTURE project aims to create the first European, ethical and GDPR-compliant (General Data Protection Regulation), quality-controlled platform, in which both large-scale data and AI algorithms will coexist. The BIGPICTURE platform will be developed in a sustainable and inclusive way by connecting communities of pathologists, researchers, AI developers, patients, and industry parties.


Machine Learning

With increasing availability of large amounts of medical data, there is a development towards data-driven medicine. We have been using machine learning for image segmentation, tissue and cell classification, and other tasks, and we are extending our expertise towards deep learning technologies.





Radiomics discovers relationships between image features and clinical data. We apply techniques from statistics and machine learning to predict patient outcomes such as survival, therapy response, or side effects. The images are either characterized with engineered features or are directly fed into a neural network.