Search Results for author: Tamara T. Mueller

Found 12 papers, 4 papers with code

Bounding Reconstruction Attack Success of Adversaries Without Data Priors

no code implementations20 Feb 2024 Alexander Ziller, Anneliese Riess, Kristian Schwethelm, Tamara T. Mueller, Daniel Rueckert, Georgios Kaissis

When training ML models with differential privacy (DP), formal upper bounds on the success of such reconstruction attacks can be provided.

Reconstruction Attack

Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging

no code implementations5 Dec 2023 Alexander Ziller, Tamara T. Mueller, Simon Stieger, Leonhard Feiner, Johannes Brandt, Rickmer Braren, Daniel Rueckert, Georgios Kaissis

Although a lower budget decreases the risk of information leakage, it typically also reduces the performance of such models.

A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regression

1 code implementation26 Sep 2023 Kyriaki-Margarita Bintsi, Tamara T. Mueller, Sophie Starck, Vasileios Baltatzis, Alexander Hammers, Daniel Rueckert

We conclude that static graph construction approaches are potentially insufficient for the task of brain age estimation and make recommendations for alternative research directions.

Age Estimation Graph Attention +1

Constructing Population-Specific Atlases from Whole Body MRI: Application to the UKBB

no code implementations28 Aug 2023 Sophie Starck, Vasiliki Sideri-Lampretsa, Jessica J. M. Ritter, Veronika A. Zimmer, Rickmer Braren, Tamara T. Mueller, Daniel Rueckert

We demonstrate different applications of these atlases, using the differences between subjects with medical conditions such as diabetes and cardiovascular diseases and healthy subjects from the atlas space.

Extended Graph Assessment Metrics for Graph Neural Networks

no code implementations13 Jul 2023 Tamara T. Mueller, Sophie Starck, Leonhard F. Feiner, Kyriaki-Margarita Bintsi, Daniel Rueckert, Georgios Kaissis

In this work, we introduce extended graph assessment metrics (GAMs) for regression tasks and continuous adjacency matrices.

regression

Body Fat Estimation from Surface Meshes using Graph Neural Networks

no code implementations13 Jul 2023 Tamara T. Mueller, Siyu Zhou, Sophie Starck, Friederike Jungmann, Alexander Ziller, Orhun Aksoy, Danylo Movchan, Rickmer Braren, Georgios Kaissis, Daniel Rueckert

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.

Interpretable 2D Vision Models for 3D Medical Images

1 code implementation13 Jul 2023 Alexander Ziller, Ayhan Can Erdur, Marwa Trigui, Alp Güvenir, Tamara T. Mueller, Philip Müller, Friederike Jungmann, Johannes Brandt, Jan Peeken, Rickmer Braren, Daniel Rueckert, Georgios Kaissis

Training Artificial Intelligence (AI) models on 3D images presents unique challenges compared to the 2D case: Firstly, the demand for computational resources is significantly higher, and secondly, the availability of large datasets for pre-training is often limited, impeding training success.

How Do Input Attributes Impact the Privacy Loss in Differential Privacy?

no code implementations18 Nov 2022 Tamara T. Mueller, Stefan Kolek, Friederike Jungmann, Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Daniel Rueckert, Georgios Kaissis

Differential privacy (DP) is typically formulated as a worst-case privacy guarantee over all individuals in a database.

Differentially Private Graph Classification with GNNs

1 code implementation5 Feb 2022 Tamara T. Mueller, Johannes C. Paetzold, Chinmay Prabhakar, Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis

In this work, we introduce differential privacy for graph-level classification, one of the key applications of machine learning on graphs.

BIG-bench Machine Learning Graph Classification

Partial sensitivity analysis in differential privacy

1 code implementation22 Sep 2021 Tamara T. Mueller, Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Friederike Jungmann, Daniel Rueckert, Georgios Kaissis

However, while techniques such as individual R\'enyi DP (RDP) allow for granular, per-person privacy accounting, few works have investigated the impact of each input feature on the individual's privacy loss.

Image Classification

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