DeSBi Retreat 2025
8 - 9 July 2025
This year, the DeSBi community gathered for its annual retreat at the Dorint Hotel in Potsdam on 8–9 July 2025. The research conducted by the seven teams of the RU was presented in the form of poster sessions, which provided ample opportunity for in-depth discussion thanks to several rounds of presentations.
DeSBi Retreat 2024
7 - 8 July 2024
Our research unit, RU KI-FOR 5363, recently held its annual retreat—an event we eagerly anticipate each year. This retreat serves as a critical platform for promoting scientific exchange and taking full advantage of the diverse expertise within our project teams. More than just a gathering, it’s a space where we come together to discuss the progress of our work, showcase key achievements, and engage in meaningful discussions that drive innovation forward.
DAGStat 2025
24 - 28 March 2025
The DeSBi Research Unit organized a dedicated session on its research topics at DAGStat 2025 in Berlin. The session, titled "Fusing Deep Learning and Statistics Towards Understanding Structured Biomedical Data," was chaired by our associated postdoctoral researcher, Georg Keilbar. Oral presentations were delivered by unit PhD students and postdocs: Masoumeh Javanbakht, Marco Simancher, Manuel Pfeuffer, and Sepideh Saran.
DeSBi Joint Seminar Series
KI-FOR 5363 DeSBi
Fusing Deep Learning and Statistics towards Understanding Structured Biomedical Data (DeSBi)
Publications
Better than Average: Spatially-Aware Aggregation of Segmentation Uncertainty Improves Downstream Performance (Guarino, V. E., Winklmayr, C., Franzen, J., Rumberger, J. L., Pfeuffer, M., Greven, S., Maier-Hein, K., Lüth, C. T., Karg, C., & Kainmüller, D.)
Published in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR; Highlight), 13145--13156
Abstract: Uncertainty Quantification (UQ) is crucial for ensuring the reliability of automated image segmentations in safety-critical domains like biomedical image analysis or autonomous driving. In segmentation, UQ generates pixel-wise uncertainty scores that must be aggregated into image-level scores for downstream tasks like Out-of-Distribution (OoD) or failure detection. Despite routine use of aggregation strategies, their properties and impact on downstream task performance have not yet been comprehensively studied. Global Average is the default choice, yet it does not account for spatial and structural features of segmentation uncertainty. Alternatives like patch-, class- and threshold-based strategies exist, but lack systematic comparison, leading to inconsistent reporting and unclear best practices. We address this gap by (1) formally analyzing properties, limitations, and pitfalls of common strategies; (2) proposing novel strategies that incorporate spatial uncertainty structure and (3) benchmarking their performance on OoD and failure detection across ten datasets that vary in image geometry and structure. We find that aggregators leveraging spatial structure yield stronger performance in both downstream tasks studied. However, the performance of individual aggregators depends heavily on dataset characteristics, so we (4) propose a meta-aggregator that integrates multiple aggregators and performs robustly across datasets.
Deep Nonparametric Conditional Independence Tests for Images (Marco Simnacher, Xiangnan Xu, Hani Park, Christoph Lippert, Sonja Greven)
Published in: Journal of Machine Learning Research 27 (2026) 1-73
Abstract: Conditional independence tests (CITs) test for conditional dependence between random variables given a vector of conditioning or confounder variables. As existing CITs are limited in their applicability to complex, high-dimensional variables such as images, we introduce deep nonparametric CITs (DNCITs). The DNCITs combine embedding maps, which extract feature representations of high-dimensional variables, with nonparametric CITs applicable to these feature representations. For the embedding maps, we derive general properties on their parameter estimators to obtain valid DNCITs and show that these properties include embedding maps learned through (conditional) unsupervised or transfer learning. For the nonparametric CITs, appropriate tests are selected and adapted to be applicable to feature representations. Through simulations, we investigate the performance of the DNCITs for different embedding maps and nonparametric CITs under varying confounder dimensions and confounder relationships. We apply the DNCITs to brain MRI scans and behavioral traits, given confounders, of healthy individuals from the UKBiobank, confirming null results from a number of ambiguous personality neuroscience studies, now with a larger data set and with our more powerful tests. In addition, in a confounder control study, we apply the DNCITs to brain MRI scans and a confounder set to test for sufficient confounder control. We provide an R package implementing the proposed DNCITs.
Pioneering newpaths: theroleofgenerativemodelling inneurological disease research (Seiler, M., & Ritter, K.)
Published in: Pflügers Archiv- European Journal of Physiology (2025) 477:571–589
Abstract: Recently, deep generative modelling has become an increasingly powerful tool with seminal work in a myriad of disciplines. This powerful modelling approach is supposed to not only have the potential to solve current problems in the medical field but also to enable personalised precision medicine and revolutionise healthcare through applications such as digital twins of patients. Here, the core concepts of generative modelling and popular modelling approaches are first introduced to consider the potential based on methodological concepts for the generation of synthetic data and the ability to learn a representation of observed data. These potentials will be reviewed using current applications in neuroimaging for data synthesis and disease decomposition in Alzheimer’s disease and multiple sclerosis. Finally, challenges for further research and applications will be discussed, including computational and data requirements, model evaluation, and potential privacy risks.