Daniel Rueckert

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Director, Alexander von Humboldt Professor for AI

Technical University of Munich

Germany

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Daniel Rueckert is the Director and Alexander von Humboldt Professor for AI at the Technical University of Munich, Germany. His research encompasses advanced areas of medical imaging, including cardiac MRI, electrocardiograms, and deep learning applications in neonatal brain reconstruction. He has published several recent articles focusing on topics such as self-supervised feature learning, weakly supervised object detection in medical images, and the use of artificial intelligence in medicine.

Recent Grants

Grant: Close

A computer-guided imaging system for prenatal screening and comprehensive diagnosis of fetal abnormalities

Open Date: 2021-10-04

Close Date: 2023-10-03

Grant: Close

Deep Learning for Medical Imaging: Learning Clinically Useful Information from Images

Open Date: 2021-01-01

Close Date: 2025-12-31

Grant: Open

UKRI Centre for Doctoral Training in Artificial Intelligence for Healthcare

Open Date: 2019-03-31

Close Date: 2027-09-29

Grant: Close

Automated Fetal and Neonatal Movement Assessment for Very Early Health Assessment

Open Date: 2019-03-31

Close Date: 2022-03-30

Grant: Close

Deciphering AMD by deep phenotyping and machine learning

Open Date: 2019-01-01

Close Date: 2024-12-31

Daniel Rueckert

Research Interests

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About

Recent Grants

Articles (30)

Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging

The Developing Human Connectome Project: A fast deep learning-based pipeline for neonatal cortical surface reconstruction

Topology Optimization in Medical Image Segmentation with Fast χ Euler Characteristic

Towards cardiac MRI foundation models: Comprehensive visual-tabular representations for whole-heart assessment and beyond

Self-Supervised Feature Learning for Cardiac Cine MR Image Reconstruction

Neural network surrogate and projected gradient descent for fast and reliable finite element model calibration: A case study on an intervertebral disc

The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review

Weakly Supervised Object Detection in Chest X-Rays With Differentiable ROI Proposal Networks and Soft ROI Pooling

CINeMA: Conditional Implicit Neural Multi-Modal Atlas for a Spatio-Temporal Representation of the Perinatal Brain

Diff-Def: Diffusion-Generated Deformation Fields for Conditional Atlases

Federated learning is not a cure-all for data ethics

Attention-Aware Non-Rigid Image Registration for Accelerated MR Imaging

3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression From Longitudinal OCTs

Morph-SSL: Self-Supervision With Longitudinal Morphing for Forecasting AMD Progression From OCT Volumes

Motion-Compensated MR CINE Reconstruction With Reconstruction-Driven Motion Estimation

Retinal vasculature of different diameters and plexuses exhibit distinct vulnerability in varying severity of diabetic retinopathy

Evaluation of deep learning estimation of whole heart anatomy from automated cardiovascular magnetic resonance short- and long-axis analyses in UK Biobank

DeepMesh: Mesh-Based Cardiac Motion Tracking Using Deep Learning

Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging

CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy

Concurrent ischemic lesion age estimation and segmentation of CT brain using a Transformer-based network

Fast fetal head compounding from multi-view 3D ultrasound

CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs

A deep learning method for replicate-based analysis of chromosome conformation contacts using Siamese neural networks

Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks

Differentially Private Graph Neural Networks for Whole-Graph Classification

Causality-Inspired Single-Source Domain Generalization for Medical Image Segmentation

Generative myocardial motion tracking via latent space exploration with biomechanics-informed prior

Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging

Unsupervised Pathology Detection: A Deep Dive Into the State of the Art

Collaborators (24)

Johannes C. Paetzold

Kerstin Hammernik

Paul Bentley

Wenjia Bai

Julia Anne Schnabel

Mengyun Qiao

Ronald Cornet

Sven Nebelung

Patrick Krumm

Boris Lenhard

Marieke Bak

Mehmet Akcakaya

Chen Qin

Bernhard Kainz

Emily Skelton

Amanda Fisher

Jacqueline Matthew

Chen Chen

Emma Claire Robinson

Matthias Merkenschlager

Taha Emre

Adam Marcus

Eimo Martens

Alistair Young

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