Efficient, high-performance pancreatic segmentation using multi-scale feature extraction. - Moritz Knolle; Georgios Kaissis; Friederike Jungmann; Sebastian Ziegelmayer; Daniel Sasse; Marcus R. Makowski; Daniel Rueckert; Rickmer Braren (CoRR, 2020)

Differentially private federated deep learning for multi-site medical image segmentation. - Alexander Ziller; Dmitrii Usynin; Nicolas Remerscheid; Moritz Knolle; Marcus R. Makowski; Rickmer Braren; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation. - Alexander Ziller; Dmitrii Usynin; Moritz Knolle; Kritika Prakash; Andrew Trask; Rickmer Braren; Marcus R. Makowski; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Differentially private training of neural networks with Langevin dynamics forcalibrated predictive uncertainty. - Moritz Knolle; Alexander Ziller; Dmitrii Usynin; Rickmer Braren; Marcus R. Makowski; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

An automatic differentiation system for the age of differential privacy. - Dmitrii Usynin; Alexander Ziller; Moritz Knolle; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Partial sensitivity analysis in differential privacy. - Tamara T. Mueller; Alexander Ziller; Dmitrii Usynin; Moritz Knolle; Friederike Jungmann; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Complex-valued deep learning with differential privacy. - Alexander Ziller; Dmitrii Usynin; Moritz Knolle; Kerstin Hammernik; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

How Do Input Attributes Impact the Privacy Loss in Differential Privacy? - Tamara T. Mueller; Stefan Kolek; Friederike Jungmann; Alexander Ziller; Dmitrii Usynin; Moritz Knolle; Daniel Rueckert; Georgios Kaissis (CoRR, 2022)

Unified Interpretation of the Gaussian Mechanism for Differential Privacy Through the Sensitivity Index. - Georgios Kaissis; Moritz Knolle; Friederike Jungmann; Alexander Ziller; Dmitrii Usynin; Daniel Rueckert (J. Priv. Confidentiality, 2022)

Diagnosis. - Daniel Rueckert; Moritz Knolle; Nicolas Duchateau; Reza Razavi; Georgios Kaissis (AI and Big Data in Cardiology, 2023)

Bias-Aware Minimisation: Understanding and Mitigating Estimator Bias in Private SGD. - Moritz Knolle; Robert Dorfman; Alexander Ziller; Daniel Rueckert; Georgios Kaissis (CoRR, 2023)

(Predictable) Performance Bias in Unsupervised Anomaly Detection. - Felix Meissen; Svenja Breuer; Moritz Knolle; Alena Buyx; Ruth Müller; Georgios Kaissis; Benedikt Wiestler; Daniel Rueckert (CoRR, 2023)

SoK: Memorisation in machine learning. - Dmitrii Usynin; Moritz Knolle; Georgios Kaissis (CoRR, 2023)

Visual Privacy Auditing with Diffusion Models. - Kristian Schwethelm; Johannes Kaiser; Moritz Knolle; Daniel Rueckert; Georgios Kaissis; Alexander Ziller (CoRR, 2024)

Memorisation in Machine Learning: A Survey of Results. - Dmitrii Usynin; Moritz Knolle; Georgios Kaissis (Trans. Mach. Learn. Res., 2024)

Visual Privacy Auditing with Diffusion Models. - Kristian Schwethelm; Johannes Kaiser; Moritz Knolle; Sarah Lockfisch; Daniel Rueckert; Alexander Ziller (Trans. Mach. Learn. Res., 2025)