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09/06/2023
MAD: Modality Agnostic Distance Measure for Image Registration
Multi-modal image registration is a crucial pre-processing step in many ...
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09/05/2023
Anatomy-Driven Pathology Detection on Chest X-rays
Pathology detection and delineation enables the automatic interpretation...
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08/23/2023
Bias-Aware Minimisation: Understanding and Mitigating Estimator Bias in Private SGD
Differentially private SGD (DP-SGD) holds the promise of enabling the sa...
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07/13/2023
Privacy-Utility Trade-offs in Neural Networks for Medical Population Graphs: Insights from Differential Privacy and Graph Structure
We initiate an empirical investigation into differentially private graph...
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07/13/2023
Extended Graph Assessment Metrics for Graph Neural Networks
When re-structuring patient cohorts into so-called population graphs, in...
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07/13/2023
Explainable 2D Vision Models for 3D Medical Data
Training Artificial Intelligence (AI) models on three-dimensional image ...
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07/08/2023
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy
We explore Reconstruction Robustness (ReRo), which was recently proposed...
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06/10/2023
Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy
Developing robust and effective artificial intelligence (AI) models in m...
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05/04/2023
Leveraging gradient-derived metrics for data selection and valuation in differentially private training
Obtaining high-quality data for collaborative training of machine learni...
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04/17/2023
Interactive and Explainable Region-guided Radiology Report Generation
The automatic generation of radiology reports has the potential to assis...
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03/03/2023
Robust Detection Outcome: A Metric for Pathology Detection in Medical Images
Detection of pathologies is a fundamental task in medical imaging and th...
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03/01/2023
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art
Deep unsupervised approaches are gathering increased attention for appli...
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02/03/2023
Private, fair and accurate: Training large-scale, privacy-preserving AI models in radiology
Artificial intelligence (AI) models are increasingly used in the medical...
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01/30/2023
Equivariant Differentially Private Deep Learning
The formal privacy guarantee provided by Differential Privacy (DP) bound...
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12/02/2022
Membership Inference Attacks Against Semantic Segmentation Models
Membership inference attacks aim to infer whether a data record has been...
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11/18/2022
How Do Input Attributes Impact the Privacy Loss in Differential Privacy?
Differential privacy (DP) is typically formulated as a worst-case privac...
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11/14/2022
The Role of Local Alignment and Uniformity in Image-Text Contrastive Learning on Medical Images
Image-text contrastive learning has proven effective for pretraining med...
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11/08/2022
Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients
The prediction of pancreatic ductal adenocarcinoma therapy response is a...
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10/24/2022
Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens
Differential Privacy (DP) provides tight upper bounds on the capabilitie...
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10/11/2022
Label Noise-Robust Learning using a Confidence-Based Sieving Strategy
In learning tasks with label noise, boosting model robustness against ov...
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09/30/2022
Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning
Normalization is an important but understudied challenge in privacy-rela...
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09/09/2022
Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis
Machine learning with formal privacy-preserving techniques like Differen...
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08/23/2022
Unsupervised Anomaly Localization with Structural Feature-Autoencoders
Unsupervised Anomaly Detection has become a popular method to detect pat...
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05/20/2022
Kernel Normalized Convolutional Networks
Existing deep convolutional neural network (CNN) architectures frequentl...
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05/09/2022
SmoothNets: Optimizing CNN architecture design for differentially private deep learning
The arguably most widely employed algorithm to train deep neural network...
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05/05/2022
Can collaborative learning be private, robust and scalable?
We investigate the effectiveness of combining differential privacy, mode...
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03/22/2022
Privacy: An axiomatic approach
The increasing prevalence of large-scale data collection in modern socie...
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03/19/2022
Relationformer: A Unified Framework for Image-to-Graph Generation
A comprehensive representation of an image requires understanding object...
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03/17/2022
SoK: Differential Privacy on Graph-Structured Data
In this work, we study the applications of differential privacy (DP) in ...
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03/01/2022
Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks
Collaborative machine learning settings like federated learning can be s...
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03/01/2022
Differentially private training of residual networks with scale normalisation
We investigate the optimal choice of replacement layer for Batch Normali...
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02/09/2022
Multi-modal unsupervised brain image registration using edge maps
Diffeomorphic deformable multi-modal image registration is a challenging...
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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...
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02/05/2022
Differentially Private Graph Classification with GNNs
Graph Neural Networks (GNNs) have established themselves as the state-of...
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01/24/2022
AutoSeg – Steering the Inductive Biases for Automatic Pathology Segmentation
In medical imaging, un-, semi-, or self-supervised pathology detection i...
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12/21/2021
Distributed Machine Learning and the Semblance of Trust
The utilisation of large and diverse datasets for machine learning (ML) ...
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12/06/2021
Joint Learning of Localized Representations from Medical Images and Reports
Contrastive learning has proven effective for pre-training image models ...
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10/07/2021
Complex-valued deep learning with differential privacy
We present ζ-DP, an extension of differential privacy (DP) to complex-va...
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09/22/2021
Partial sensitivity analysis in differential privacy
Differential privacy (DP) allows the quantification of privacy loss when...
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09/22/2021
An automatic differentiation system for the age of differential privacy
We introduce Tritium, an automatic differentiation-based sensitivity ana...
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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...
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08/30/2021
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)
Biological neural networks define the brain function and intelligence of...
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07/30/2021
NeuralDP Differentially private neural networks by design
The application of differential privacy to the training of deep neural n...
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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)...
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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...
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07/06/2021
Differentially private federated deep learning for multi-site medical image segmentation
Collaborative machine learning techniques such as federated learning (FL...
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05/21/2021
HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning
Federated learning (FL) enables multiple clients to jointly train a glob...
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01/14/2021
U-Noise: Learnable Noise Masks for Interpretable Image Segmentation
Deep Neural Networks (DNNs) are widely used for decision making in a myr...
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12/10/2020
Privacy-preserving medical image analysis
The utilisation of artificial intelligence in medicine and healthcare ha...
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09/02/2020
