Daniel Rueckert

research

∙ 08/04/2022

Metadata-enhanced contrastive learning from retinal optical coherence tomography images

Supervised deep learning algorithms hold great potential to automate scr...

Robbie Holland, et al. ∙

research

∙ 07/29/2022

MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI

Recovering the 3D motion of the heart from cine cardiac magnetic resonan...

Qingjie Meng, et al. ∙

research

∙ 07/22/2022

Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs

Optical coherence tomography angiography (OCTA) can non-invasively image...

Martin J. Menten, et al. ∙

research

∙ 06/21/2022

Faster Diffusion Cardiac MRI with Deep Learning-based breath hold reduction

Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) enables us to probe...

Michael Tanzer, et al. ∙

research

∙ 06/08/2022

What do we learn? Debunking the Myth of Unsupervised Outlier Detection

Even though auto-encoders (AEs) have the desirable property of learning ...

Cosmin I. Bercea, et al. ∙

research

∙ 05/31/2022

Surface Analysis with Vision Transformers

The extension of convolutional neural networks (CNNs) to non-Euclidean g...

Simon Dahan, et al. ∙

research

∙ 05/20/2022

Kernel Normalized Convolutional Networks

Existing deep convolutional neural network (CNN) architectures frequentl...

Reza Nasirigerdeh, et al. ∙

research

∙ 05/09/2022

SmoothNets: Optimizing CNN architecture design for differentially private deep learning

The arguably most widely employed algorithm to train deep neural network...

Nicolas W. Remerscheid, et al. ∙

research

∙ 05/05/2022

Can collaborative learning be private, robust and scalable?

We investigate the effectiveness of combining differential privacy, mode...

Dmitrii Usynin, et al. ∙

research

∙ 04/07/2022

Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces

Recent state-of-the-art performances of Vision Transformers (ViT) in com...

Simon Dahan, et al. ∙

research

∙ 03/30/2022

Surface Vision Transformers: Attention-Based Modelling applied to Cortical Analysis

The extension of convolutional neural networks (CNNs) to non-Euclidean g...

Simon Dahan, et al. ∙

research

∙ 03/23/2022

Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging

Physics-driven deep learning methods have emerged as a powerful tool for...

Kerstin Hammernik, et al. ∙

research

∙ 03/22/2022

Privacy: An axiomatic approach

The increasing prevalence of large-scale data collection in modern socie...

Alexander Ziller, et al. ∙

research

∙ 03/17/2022

SoK: Differential Privacy on Graph-Structured Data

In this work, we study the applications of differential privacy (DP) in ...

Tamara T. Mueller, et al. ∙

research

∙ 03/01/2022

Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks

Collaborative machine learning settings like federated learning can be s...

Dmitrii Usynin, et al. ∙

research

∙ 03/01/2022

Differentially private training of residual networks with scale normalisation

We investigate the optimal choice of replacement layer for Batch Normali...

Helena Klause, et al. ∙

research

∙ 02/09/2022

Multi-modal unsupervised brain image registration using edge maps

Diffeomorphic deformable multi-modal image registration is a challenging...

Vasiliki Sideri-Lampretsa, et al. ∙

research

∙ 02/08/2022

On the Pitfalls of Using the Residual Error as Anomaly Score

Many current state-of-the-art methods for anomaly localization in medica...

Felix Meissen, et al. ∙

research

∙ 02/05/2022

Differentially Private Graph Classification with GNNs

Graph Neural Networks (GNNs) have established themselves as the state-of...

Tamara T. Mueller, et al. ∙

research

∙ 01/24/2022

AutoSeg – Steering the Inductive Biases for Automatic Pathology Segmentation

In medical imaging, un-, semi-, or self-supervised pathology detection i...

Felix Meissen, et al. ∙

research

∙ 12/23/2021

AI-based Reconstruction for Fast MRI – A Systematic Review and Meta-analysis

Compressed sensing (CS) has been playing a key role in accelerating the ...

Yutong Chen, et al. ∙

research

∙ 12/21/2021

Distributed Machine Learning and the Semblance of Trust

The utilisation of large and diverse datasets for machine learning (ML) ...

Dmitrii Usynin, et al. ∙

research

∙ 12/07/2021

GraDIRN: Learning Iterative Gradient Descent-based Energy Minimization for Deformable Image Registration

We present a Gradient Descent-based Image Registration Network (GraDIRN)...

Huaqi Qiu, et al. ∙

research

∙ 12/02/2021

FedRAD: Federated Robust Adaptive Distillation

The robustness of federated learning (FL) is vital for the distributed t...

Stefán Páll Sturluson, et al. ∙

research

∙ 10/07/2021

Complex-valued deep learning with differential privacy

We present ζ-DP, an extension of differential privacy (DP) to complex-va...

Alexander Ziller, et al. ∙

research

∙ 09/30/2021

DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization

In recent years, the research landscape of machine learning in medical i...

Turkay Kart, et al. ∙

research

∙ 09/22/2021

Partial sensitivity analysis in differential privacy

Differential privacy (DP) allows the quantification of privacy loss when...

Tamara T. Mueller, et al. ∙

research

∙ 09/22/2021

An automatic differentiation system for the age of differential privacy

We introduce Tritium, an automatic differentiation-based sensitivity ana...

Dmitrii Usynin, et al. ∙

research

∙ 09/22/2021

A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index

The Gaussian mechanism (GM) represents a universally employed tool for a...

Georgios Kaissis, et al. ∙

research

∙ 09/07/2021

Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity

The study of functional brain connectivity (FC) is important for underst...

Simon Dahan, et al. ∙

research

∙ 08/11/2021

Voxel-level Importance Maps for Interpretable Brain Age Estimation

Brain aging, and more specifically the difference between the chronologi...

Kyriaki-Margarita Bintsi, et al. ∙

research

∙ 07/30/2021

NeuralDP Differentially private neural networks by design

The application of differential privacy to the training of deep neural n...

Moritz Knolle, et al. ∙

research

∙ 07/19/2021

Transductive image segmentation: Self-training and effect of uncertainty estimation

Semi-supervised learning (SSL) uses unlabeled data during training to le...

Konstantinos Kamnitsas, et al. ∙

research

∙ 07/16/2021

Joint Semi-supervised 3D Super-Resolution and Segmentation with Mixed Adversarial Gaussian Domain Adaptation

Optimising the analysis of cardiac structure and function requires accur...

Nicolo' Savioli, et al. ∙

research

∙ 07/09/2021

Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty

We show that differentially private stochastic gradient descent (DP-SGD)...

Moritz Knolle, et al. ∙

research

∙ 07/09/2021

Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation

In recent years, formal methods of privacy protection such as differenti...

Alexander Ziller, et al. ∙

research

∙ 07/06/2021

Differentially private federated deep learning for multi-site medical image segmentation

Collaborative machine learning techniques such as federated learning (FL...

Alexander Ziller, et al. ∙

research

∙ 05/25/2021

Learning a Model-Driven Variational Network for Deformable Image Registration

Data-driven deep learning approaches to image registration can be less a...

Xi Jia, et al. ∙

research

∙ 05/22/2021

Automated Knee X-ray Report Generation

Gathering manually annotated images for the purpose of training a predic...

Aydan Gasimova, et al. ∙

research

∙ 05/21/2021

HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning

Federated learning (FL) enables multiple clients to jointly train a glob...

Reza Nasirigerdeh, et al. ∙

research

∙ 03/05/2021

FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation

In recent years, data-driven machine learning (ML) methods have revoluti...

Cosmin I. Bercea, et al. ∙

research

∙ 01/12/2021

Personalized Federated Deep Learning for Pain Estimation From Face Images

Standard machine learning approaches require centralizing the users' dat...

Ognjen Rudovic, et al. ∙

research

∙ 12/10/2020

Privacy-preserving medical image analysis

The utilisation of artificial intelligence in medicine and healthcare ha...

Alexander Ziller, et al. ∙

research

∙ 11/30/2020

Reducing Textural Bias Improves Robustness of Deep Segmentation CNNs

Despite current advances in deep learning, domain shift remains a common...

Seoin Chai, et al. ∙

research

∙ 11/15/2020

2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments

Federated Learning harnesses data from multiple sources to build a singl...

Harry Cai, et al. ∙

research

∙ 11/10/2020

A Systematic Comparison of Encrypted Machine Learning Solutions for Image Classification

This work provides a comprehensive review of existing frameworks based o...

Veneta Haralampieva, et al. ∙

research

∙ 10/30/2020

Mutual Information-based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging

Deep neural networks exhibit limited generalizability across images with...

Qingjie Meng, et al. ∙

research

∙ 09/17/2020

Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare

Federated learning (FL) has enabled training models collaboratively from...

Matei Grama, et al. ∙

research

∙ 09/02/2020

Efficient, high-performance pancreatic segmentation using multi-scale feature extraction

For artificial intelligence-based image analysis methods to reach clinic...

Moritz Knolle, et al. ∙

research

∙ 08/29/2020

Patch-based Brain Age Estimation from MR Images

Brain age estimation from Magnetic Resonance Images (MRI) derives the di...

Kyriaki-Margarita Bintsi, et al. ∙

Daniel Rueckert

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