7 citations · 9 across the 11 of their papers we have counts for
11 papers
Parametric shape models for vessels learned from segmentations via differentiable voxelization
Alina F. Dima, Suprosanna Shit, Huaqi Qiu +8
Vessels are complex structures in the body that have been studied extensively in multiple representations. While voxelization is the most common of them, meshes and parametric mode…
Diff-Def: Diffusion-Generated Deformation Fields for Conditional Atlases
Sophie Starck, Vasiliki Sideri-Lampretsa, Bernhard Kainz +3
Anatomical atlases are widely used for population studies and analysis. Conditional atlases target a specific sub-population defined via certain conditions, such as demographics or…
From Mean to Extreme: Formal Differential Privacy Bounds on the Success of Real-World Data Reconstruction Attacks
Anneliese Riess, Kristian Schwethelm, Johannes Kaiser +4
The gold standard for privacy in machine learning, Differential Privacy (DP), is often interpreted through its guarantees against membership inference. However, translating DP budg…
Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging
Alexander Ziller, Tamara T. Mueller, Simon Stieger +5
Artificial Intelligence (AI) models are vulnerable to information leakage of their training data, which can be highly sensitive, for example in medical imaging. Privacy Enhancing T…
A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regression
Kyriaki-Margarita Bintsi, Tamara T. Mueller, Sophie Starck +3
The difference between the chronological and biological brain age of a subject can be an important biomarker for neurodegenerative diseases, thus brain age estimation can be crucia…
Using UK Biobank data to establish population-specific atlases from whole body MRI
Sophie Starck, Vasiliki Sideri-Lampretsa, Jessica J. M. Ritter +4
Reliable reference data in medical imaging is largely unavailable. Developing tools that allow for the comparison of individual patient data to reference data has a high potential…
Body Fat Estimation from Surface Meshes using Graph Neural Networks
Tamara T. Mueller, Siyu Zhou, Sophie Starck +7
Body fat volume and distribution can be a strong indication for a person's overall health and the risk for developing diseases like type 2 diabetes and cardiovascular diseases. Fre…
Privacy-Utility Trade-offs in Neural Networks for Medical Population Graphs: Insights from Differential Privacy and Graph Structure
Tamara T. Mueller, Maulik Chevli, Ameya Daigavane +2
We initiate an empirical investigation into differentially private graph neural networks on population graphs from the medical domain by examining privacy-utility trade-offs at dif…
Extended Graph Assessment Metrics for Graph Neural Networks
Tamara T. Mueller, Sophie Starck, Leonhard F. Feiner +3
When re-structuring patient cohorts into so-called population graphs, initially independent data points can be incorporated into one interconnected graph structure. This population…
Atlas-Based Interpretable Age Prediction In Whole-Body MR Images
Sophie Starck, Yadunandan Vivekanand Kini, Jessica Johanna Maria Ritter +3
Age prediction is an important part of medical assessments and research. It can aid in detecting diseases as well as abnormal ageing by highlighting potential discrepancies between…
Interpretable 2D Vision Models for 3D Medical Images
Alexander Ziller, Ayhan Can Erdur, Marwa Trigui +9
Training Artificial Intelligence (AI) models on 3D images presents unique challenges compared to the 2D case: Firstly, the demand for computational resources is significantly highe…