Research projects
Research at the Chair for Pattern Recognition
Our research is supported by a number of third-party funding from industry and public funding agencies, including the German Research Foundation (DFG), the Federal Ministry of Research, Technology and Space (BMFTR), and the Bavarian Ministry of Health, Care and Prevention.
Principal Investigator in our group: Prof. Dr.-Ing. Katharina Breininger
Academic Partners: Prof. Dr.-Ing. Franziska Mathis-Ullrich, PD Dr.-med. Stefanie Burghaus, Prof. Dr.-Ing. Jana Hutter, Dr.-Ing. Sina Martin, Prof. Dr. Matthias May, Prof. Dr. Maria Rentetzi, Prof. Dr. Julia Schnabel
Collaborating institutions: FAU Erlangen-Nürnberg, University Hospital Erlangen, Technische Universität München, Leibniz-Universität Hannover, Endometriosevereinigung Deutschland e.V.
Project duration: 2025-now
Funding agency: Funded by the Bavarian Minstery of Health, Care and Prevention
Link to CRC project page: https://www.crc1540-ebm.research.fau.eu/
The central nervous system (CNS) is our most complex organ system. Despite tremendous progress in our understanding of the biochemical, electrical, and genetic regulation of CNS functioning and malfunctioning, many fundamental processes and diseases are still not fully understood. The CRC 1540 ‘Exploring Brain Mechanics’ synergizes the expertise of engineers, physicists, biologists, medical researchers, and clinicians in Erlangen, Berlin and beyond to exploit mechanics-based approaches to advance our understanding of CNS function and, as a long-term vision, to provide the foundation for future improvement of diagnosis and treatment of neurological disorders.
In the subproject X02: Data analysis and machine learning for heterogeneous, cross-species data, we aim to integrate state-of-the-art machine learning and image analysis as a core component in the research project.
Machine Learning (ML) and specifically Deep Learning (DL) has revolutionised signal and especially image processing, with unprecedented possibilities to automate quantitative analysis. While the methods developed in this field translate well across a variety of tasks, they often need large amounts of data and especially data with corresponding high-quality ground truth correspondences (gold standard measurements/labels/annotations) to adapt the parameters of the underlying models for each new application. Within the EBM consortium, a wealth of genetic, biochemical, mechanical, and imaging data will be acquired in different projects, spanning different species, experimental settings, and modalities. To utilise this data to its full extent and support quantification of experimental results, project X02 will firstly generate tools and models to integrate machine learning and deep learning techniques within the projects of this collaborative research consortium (CRC). Furthermore, we will promote and provide expertise for the use and release of collected data to online open data repositories to interface with the global research community. Secondly, X02 will use the data acquired within EBM to investigate methods to transfer knowledge under different domain shifts, and thereby enable novel insights into measurement modalities like Brillouin microscopy and atomic force microscopy (AFM)-based nanoindentation in combination with histological analysis and fluorescence imaging.
Link to project page: https://www.kfo5024.med.fau.de/
Project duration: 2023-now
Principal Investigator in our group: Prof. Dr.-Ing. Katharina Breininger
Project partners: Prof. Dr. med. Stefan Uderhardt (UK Erlangen), KFO5024-Consortium from the University Hospital Erlangen and FAU Erlangen-Nürnberg lead by Prof. Dr. Beate Winner and Prof. Dr. Claudia Günther
