Third party funded individual grant
Acronym: DFG 512819079
Start date : 01.01.2024
End date : 31.01.2024
Website: https://gepris.dfg.de/gepris/projekt/512819079
Deep learning has emerged as a key technology in biomedical image analysis, but is difficult to handle for non-experts due to the high demands on computing power and data management. This project will develop a platform to facilitate large-scale statistical analysis of multimodal biomedical imaging and patient data using state-of-the-art deep learning methods. The proposed infrastructure will provide access to state-of-the-art algorithms and define a standardized data infrastructure that can be easily deployed in heterogeneous environments. Our prototype will provide an effective mechanism for sharing pre-trained algorithms and advanced analytical tools. The platform is aimed at the biomedical research community and will provide scientists with novel, powerful and validated tools to address challenges such as image-based disease phenotyping and predictive modeling. State-of-the-art analysis pipelines will be implemented and packaged into user-friendly toolboxes that can be directly used in clinical workflows and enable the extraction of imaging biomarkers and quantitative measurements. Our approach is based on three basic principles: Data linkage (across systems), data stewardship (patient privacy and legal/ethical compliance) and data interoperability (use of public APls and open standards). To achieve this, we will build on an existing model: Data will be kept in a secure environment, using AI algorithms to train with sensitive patient data within our clinic's firewall. Two approaches will be supported: a secure learning orchestration server that will provide learning coordination for secure data enclaves at our partner hospital, the University Hospital Erlangen (UKER), and secure sandboxes that will enable model development in a secure environment hosted by the university at FAU. As in a federated learning paradigm, most of the models will move through our infrastructure, not the data. We will set up (and support) the infrastructure in these environments with support from the Department of Artificial Intelligence in Biomedical Engineering (AIBE) at FAU, the Radiology Department at UKER and the Regional Computing Center Erlangen (RRZE) to provide the required capabilities. Our proposed solution is highly interoperable and scalable for other clinics and enables integration e.g. with the Medical Informatics Initiative. This approach will provide secure and compliant access to the clinics' PACS and electronic patient records, and enable reproducible research.
Deep learning has emerged as a key technology in biomedical image analysis, but is difficult to handle for non-experts due to the high demands on computing power and data management. This project will develop a platform to facilitate large-scale statistical analysis of multimodal biomedical imaging and patient data using state-of-the-art deep learning methods. The proposed infrastructure will provide access to state-of-the-art algorithms and define a standardized data infrastructure that can be easily deployed in heterogeneous environments. Our prototype will provide an effective mechanism for sharing pre-trained algorithms and advanced analytical tools. The platform is aimed at the biomedical research community and will provide scientists with novel, powerful and validated tools to address challenges such as image-based disease phenotyping and predictive modeling. State-of-the-art analysis pipelines will be implemented and packaged into user-friendly toolboxes that can be directly used in clinical workflows and enable the extraction of imaging biomarkers and quantitative measurements. Our approach is based on three basic principles: Data linkage (across systems), data stewardship (patient privacy and legal/ethical compliance) and data interoperability (use of public APls and open standards). To achieve this, we will build on an existing model: Data will be kept in a secure environment, using AI algorithms to train with sensitive patient data within our clinic's firewall. Two approaches will be supported: a secure learning orchestration server that will provide learning coordination for secure data enclaves at our partner hospital, the University Hospital Erlangen (UKER), and secure sandboxes that will enable model development in a secure environment hosted by the university at FAU. As in a federated learning paradigm, most of the models will move through our infrastructure, not the data. We will set up (and support) the infrastructure in these environments with support from the Department of Artificial Intelligence in Biomedical Engineering (AIBE) at FAU, the Radiology Department at UKER and the Regional Computing Center Erlangen (RRZE) to provide the required capabilities. Our proposed solution is highly interoperable and scalable for other clinics and enables integration e.g. with the Medical Informatics Initiative. This approach will provide secure and compliant access to the clinics' PACS and electronic patient records, and enable reproducible research.