Daniel Rueckert

This research develops atlases to analyze health variations in populations using MRI scans.

Researchers use graph neural networks to better predict MS disease activity.

NISF enhances anatomical shape segmentation in medical imaging with improved accuracy.

A look into reconstruction attacks and their impact on data privacy in machine learning.

New deep learning method improves MRI accuracy for heart tissue mapping.

New strategies simplify image-to-graph transformation for various applications.

Examining the impact of data priors on differential privacy in image reconstruction attacks.

This study uses deep learning to find new biomarkers for age-related macular degeneration.

An analysis of language models and their role in healthcare.

MORPHADE provides innovative brain analysis for improved Alzheimer's detection.

A new model improves eye disease evaluations and patient referrals.

A fresh method to compare privacy mechanisms in machine learning.

Exploring new methods in medical image registration and their effectiveness.

Understanding counterfactual explanations to improve medical diagnosis through AI.

A new method enhances accuracy and speed in spine model calibration using machine learning.

Topograph offers a fast method for image segmentation while keeping shapes and connections intact.

Learn to make the most of limited data for language models.

New methods are transforming how medical images are aligned for better diagnoses.

A groundbreaking method captures real-time heart movements with enhanced clarity.

Exploring the importance of topology in efficient image segmentation methods.