Prof. Dr. Lars Linsen, Institut für Informatik

The multiscale imaging strategy central to this initiative imposes novel data analysis challenges. The high complexity of the acquired data results from their nature of being volumetric, time-varying, large, multiscale, and forming cohorts). Meeting these challenges requires basic research in the fields of image analysis, machine learning, and visualization. Machine learning will be used to uncover inherent relationships between patterns at multiple scales. An interactive visual approach supports the user-centric analysis of detected features.

The deliverable of this project will be generally applicable, effective, and efficient methods supporting the overall goal of multiscale data analysis.

Clinical practice often focuses on the investigation of one single disease, while the health status of a human is much more complex and may depend on many factors. Recently, cohort studies have been introduced to investigate, in longitudinal studies, the health status of an entire population (the cohort) by capturing health record data, whole-body medical imaging data, personal data including socio-economical circumstances, and even genetic sequencing data. Given this large amount of heterogeneous data, there is a lack of proper tools for its multi-variate analysis. In this project, we propose novel interactive visual analysis methods for testing hypotheses, supporting the generation of new hypotheses, and investigating changes over time. The goal is to allow for the detection of risk or biomarkers and even genetic associations in a multi-variate setting.In the second funding period, the research conducted in the first funding period shall be enhanced in various aspects. We will put a particular focus on the time aspect in multi-dimensional heterogeneous data from longitudinal studies, the analysis of influencing factors, analyzing multi-dimensional heterogeneous data with missing entries, and analyzing sparse high-dimensional data from genome-wide association studies.Moreover, we would like to validate the effectiveness of the proposed analysis methods by performing comparative visual analyses of the multi-dimensional heterogeneous data from different cohort studies.