Funding

Transform Liver - Scaling up Vision Transformers for Biomarkers in Liver Disease

Liver diseases are widespread in Germany and Europe and are an increasingly important cause of sickness and mortality. Computer-aided assistance systems can use artificial intelligence (AI) to contribute to early detection and therapy decisions in liver diseases, for example by processing image and tabular data.

In this project, the partners are developing Vision Transformer (ViT, a deep learning architecture), the latest and most powerful type of artificial neural network, specifically for the diagnosis and risk prediction of liver disease. They are building on results from non-medical fields that have not yet been applied to medical research. For the first time, the researchers are enabling the systematic use of the new transformer technology for medical issues.

The consortium has a large collection of image data and associated patient data such as age, gender and comorbidities. This data will be used to train new AI models (transformers). The goal is to generate new biomarkers to predict disease progression and provide information for personalised patient treatment. These biomarkers are expected to outperform classical approaches based on conventional neural networks. In addition, TRANSFORM LIVER will provide a better understanding of disease processes in the liver, in particular cellular interactions, by applying explanatory approaches to trained transformers.

Facts and figures

Coordinator: TU Dresden
Number of Partners: 3
Start Date: March 1, 2023
End Date: February, 28, 2026
Total Funding: € 491.788,80
Own Funding: € 306.619,17
This project has received funding from the Federal Ministry of Education and Research under grant agreement No. 031L0312C.

SWAG

Schwarmlernen und Generative Modelle zur Synthese und Nutzbarmachung hochqualitativer Daten in der Krebsmedizin - SWAG

Modern cancer research is generating more extensive data sets (big data) than ever. The data originates from molecular and biochemical analyses, modern imaging procedures, clinical studies or depicts the course of a patient's disease. These treasures of data sets need to be exploited in the future. New computer-aided approaches to use such data, namely artificial intelligence, machine learning and statistics are of great importance for the improved analysis and extraction of research-relevant information. With this funding guideline as part of the “Nationale Dekade gegen Krebs” the Federal Ministry of Education and Research (BMBF) intends to provide research groups from the field of data analysis with low-threshold access to high-quality data from translational, biomedical cancer research and routine oncological care. At the same time, researchers from the fields of data acquisition and data analysis work closely together to address clinically relevant oncological questions. In addition, the culture of data sharing for research purposes is to be promoted.

The SWAG project is developing AI methods for renal cell carcinoma that generate synthetic data from real patient data. Generative AI algorithms are trained jointly across several hospitals using swarm learning without exchanging the actual data. The resulting pseudonymized synthetic data is evaluated according to defined criteria.

Facts and figures

Coordinator: University Hospital Würzburg
Number of Partners: 5
Start Date: November 1, 2022
End Date: October 31, 2024
Own Funding: € 192.510,80
This project receives funding from the Federal Ministry of Education and Research under grant agreement No. 01KD2215B.

Radiomic analysis of DCE breast MRI data sets for improved diagnosis of breast cancer - a multi-institutional evaluation

This project develops and evaluates deep learning algorithms for breast cancer detection and diagnosis using MRI screening studies. By creating AI systems that support radiologists, it aims to overcome challenges in achieving acceptable diagnostic performance and transferability across clinical sites, enabling the widespread adoption of MRI-based breast cancer screening.

Facts and figures

Coordinator: University Hospital RWTH Aachen
Number of Partners: 0
Start Date: January 15, 2024
End Date: January 14, 2027
Total Funding: € 774.645,00
This project has received funding from the German Research Foundation (DFG) under grant agreement No. 515639690.

DFG Detection of Early Osteoarthritis

The aim of the project is the experimental and clinical-scientific evaluation of non-invasive assessment of cartilage functionality. The diagnosis of early osteoarthritis continues to present diagnostic difficulties despite advancing technical developments, so that scientific approaches to diagnosis in this area, not least against the background of emerging demographic developments in Western societies, are of great clinical relevance.

Facts and figures

Coordinator: University Hospital RWTH Aachen
Number of Partners: 0
Start Date: March 1, 2019
End Date: May 31, 2025
Total Funding: € 569.910,00
This project has received funding from the German Research Foundation (DFG) under grant agreement No. 417508432.

DFG Computational Biomechanics

The overarching objective of the proposed basic research project is the development of a constitutive model that can predict from imaging data the mechanical properties of articular cartilage (i.e., cartilage functionality). If proven successful, the model-based predictions of tissue functionality based on imaging alone may be extended to other tissues and may provide a valid and substantiated framework for the non-invasive evaluation of early-stage cartilage degeneration and OA.

Facts and figures

Coordinator: University Hospital RWTH Aachen
Number of Partners: 2
Start Date: January 1, 2024
End Date: December 31, 2027
Own Funding: € 368.890,00
This project has received funding from the German Research Foundation (DFG) under grant agreement No. 517243167.