1.2k citations · 2k across the 56 of their papers we have counts for
56 papers
Propagation and Attribution of Uncertainty in Medical Imaging Pipelines
Leonhard F. Feiner, Martin J. Menten, Kerstin Hammernik +5
Uncertainty estimation, which provides a means of building explainable neural networks for medical imaging applications, have mostly been studied for single deep learning models th…
(Predictable) Performance Bias in Unsupervised Anomaly Detection
Felix Meissen, Svenja Breuer, Moritz Knolle +5
Background: With the ever-increasing amount of medical imaging data, the demand for algorithms to assist clinicians has amplified. Unsupervised anomaly detection (UAD) models promi…
MAD: Modality Agnostic Distance Measure for Image Registration
Vasiliki Sideri-Lampretsa, Veronika A. Zimmer, Huaqi Qiu +2
Multi-modal image registration is a crucial pre-processing step in many medical applications. However, it is a challenging task due to the complex intensity relationships between d…
Anatomy-Driven Pathology Detection on Chest X-rays
Philip Müller, Felix Meissen, Johannes Brandt +2
Pathology detection and delineation enables the automatic interpretation of medical scans such as chest X-rays while providing a high level of explainability to support radiologist…
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah +117
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. I…
Bias-Aware Minimisation: Understanding and Mitigating Estimator Bias in Private SGD
Moritz Knolle, Robert Dorfman, Alexander Ziller +2
Differentially private SGD (DP-SGD) holds the promise of enabling the safe and responsible application of machine learning to sensitive datasets. However, DP-SGD only provides a bi…
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…
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy
Georgios Kaissis, Jamie Hayes, Alexander Ziller +1
We explore Reconstruction Robustness (ReRo), which was recently proposed as an upper bound on the success of data reconstruction attacks against machine learning models. Previous r…
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…
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…
Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy
Soroosh Tayebi Arasteh, Mahshad Lotfinia, Teresa Nolte +6
Developing robust and effective artificial intelligence (AI) models in medicine requires access to large amounts of patient data. The use of AI models solely trained on large multi…
Interactive and Explainable Region-guided Radiology Report Generation
Tim Tanida, Philip Müller, Georgios Kaissis +1
The automatic generation of radiology reports has the potential to assist radiologists in the time-consuming task of report writing. Existing methods generate the full report from…
Robust Detection Outcome: A Metric for Pathology Detection in Medical Images
Felix Meissen, Philip Müller, Georgios Kaissis +1
Detection of pathologies is a fundamental task in medical imaging and the evaluation of algorithms that can perform this task automatically is crucial. However, current object dete…
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art
Ioannis Lagogiannis, Felix Meissen, Georgios Kaissis +1
Deep unsupervised approaches are gathering increased attention for applications such as pathology detection and segmentation in medical images since they promise to alleviate the n…
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
Soroosh Tayebi Arasteh, Alexander Ziller, Christiane Kuhl +6
Artificial intelligence (AI) models are increasingly used in the medical domain. However, as medical data is highly sensitive, special precautions to ensure its protection are requ…
Equivariant Differentially Private Deep Learning: Why DP-SGD Needs Sparser Models
Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis
Differentially Private Stochastic Gradient Descent (DP-SGD) limits the amount of private information deep learning models can memorize during training. This is achieved by clipping…
Membership Inference Attacks Against Semantic Segmentation Models
Tomas Chobola, Dmitrii Usynin, Georgios Kaissis
Membership inference attacks aim to infer whether a data record has been used to train a target model by observing its predictions. In sensitive domains such as healthcare, this ca…
Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning
Reza Nasirigerdeh, Javad Torkzadehmahani, Daniel Rueckert +1
Normalization is an important but understudied challenge in privacy-related application domains such as federated learning (FL), differential privacy (DP), and differentially priva…
How Do Input Attributes Impact the Privacy Loss in Differential Privacy?
Tamara T. Mueller, Stefan Kolek, Friederike Jungmann +5
Differential privacy (DP) is typically formulated as a worst-case privacy guarantee over all individuals in a database. More recently, extensions to individual subjects or their at…
The Role of Local Alignment and Uniformity in Image-Text Contrastive Learning on Medical Images
Philip Müller, Georgios Kaissis, Daniel Rueckert
Image-text contrastive learning has proven effective for pretraining medical image models. When targeting localized downstream tasks like semantic segmentation or object detection,…
Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients
Alexander Ziller, Ayhan Can Erdur, Friederike Jungmann +3
The prediction of pancreatic ductal adenocarcinoma therapy response is a clinically challenging and important task in this high-mortality tumour entity. The training of neural netw…
Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens
Georgios Kaissis, Alexander Ziller, Stefan Kolek Martinez de Azagra +1
Differential Privacy (DP) provides tight upper bounds on the capabilities of optimal adversaries, but such adversaries are rarely encountered in practice. Under the hypothesis test…
Label Noise-Robust Learning using a Confidence-Based Sieving Strategy
Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Daniel Rueckert +1
In learning tasks with label noise, improving model robustness against overfitting is a pivotal challenge because the model eventually memorizes labels, including the noisy ones. I…