Daniel Rueckert

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Imperial College London

Professor; Head of the Department of Computing at Imperial College London, Consultant at Magic Pony Technology

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

Semi-supervised learning (SSL) uses unlabeled data during training to le...
07/19/2021 ∙ by Konstantinos Kamnitsas, et al. ∙ 11 ∙ share

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

Optimising the analysis of cardiac structure and function requires accur...
07/16/2021 ∙ by Nicolo' Savioli, et al. ∙ 5 ∙ share

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Differentially private training of neural networks with Langevin dynamics forcalibrated predictive uncertainty

We show that differentially private stochastic gradient descent (DP-SGD)...
07/09/2021 ∙ by Moritz Knolle, et al. ∙ 0 ∙ share

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Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation

In recent years, formal methods of privacy protection such as differenti...
07/09/2021 ∙ by Alexander Ziller, et al. ∙ 0 ∙ share

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

Collaborative machine learning techniques such as federated learning (FL...
07/06/2021 ∙ by Alexander Ziller, et al. ∙ 0 ∙ share

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Learning a Model-Driven Variational Network for Deformable Image Registration

Data-driven deep learning approaches to image registration can be less a...
05/25/2021 ∙ by Xi Jia, et al. ∙ 7 ∙ share

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Automated Knee X-ray Report Generation

Gathering manually annotated images for the purpose of training a predic...
05/22/2021 ∙ by Aydan Gasimova, et al. ∙ 0 ∙ share

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

Federated learning (FL) enables multiple clients to jointly train a glob...
05/21/2021 ∙ by Reza Nasirigerdeh, et al. ∙ 0 ∙ share

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FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation

In recent years, data-driven machine learning (ML) methods have revoluti...
03/05/2021 ∙ by Cosmin I. Bercea, et al. ∙ 15 ∙ share

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Personalized Federated Deep Learning for Pain Estimation From Face Images

Standard machine learning approaches require centralizing the users' dat...
01/12/2021 ∙ by Ognjen Rudovic, et al. ∙ 37 ∙ share

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Privacy-preserving medical image analysis

The utilisation of artificial intelligence in medicine and healthcare ha...
12/10/2020 ∙ by Alexander Ziller, et al. ∙ 16 ∙ share

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Reducing Textural Bias Improves Robustness of Deep Segmentation CNNs

Despite current advances in deep learning, domain shift remains a common...
11/30/2020 ∙ by Seoin Chai, et al. ∙ 0 ∙ share

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

Federated Learning harnesses data from multiple sources to build a singl...
11/15/2020 ∙ by Harry Cai, et al. ∙ 0 ∙ share

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A Systematic Comparison of Encrypted Machine Learning Solutions for Image Classification

This work provides a comprehensive review of existing frameworks based o...
11/10/2020 ∙ by Veneta Haralampieva, et al. ∙ 0 ∙ share

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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...
10/30/2020 ∙ by Qingjie Meng, et al. ∙ 8 ∙ share

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Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare

Federated learning (FL) has enabled training models collaboratively from...
09/17/2020 ∙ by Matei Grama, et al. ∙ 0 ∙ share

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

For artificial intelligence-based image analysis methods to reach clinic...
09/02/2020 ∙ by Moritz Knolle, et al. ∙ 16 ∙ share

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Patch-based Brain Age Estimation from MR Images

Brain age estimation from Magnetic Resonance Images (MRI) derives the di...
08/29/2020 ∙ by Kyriaki-Margarita Bintsi, et al. ∙ 12 ∙ share

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Causal Future Prediction in a Minkowski Space-Time

Estimating future events is a difficult task. Unlike humans, machine lea...
08/20/2020 ∙ by Athanasios Vlontzos, et al. ∙ 25 ∙ share

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Unsupervised Cross-domain Image Classification by Distance Metric Guided Feature Alignment

Learning deep neural networks that are generalizable across different do...
08/19/2020 ∙ by Qingjie Meng, et al. ∙ 17 ∙ share

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Communicative Reinforcement Learning Agents for Landmark Detection in Brain Images

Accurate detection of anatomical landmarks is an essential step in sever...
08/18/2020 ∙ by Guy Leroy, et al. ∙ 13 ∙ share

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Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening

Prenatal screening with ultrasound can lower neonatal mortality signific...
08/16/2020 ∙ by Jeremy Tan, et al. ∙ 37 ∙ share

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Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface

Accurate estimation of the age in neonates is essential for measuring ne...
08/13/2020 ∙ by Vitalis Vosylius, et al. ∙ 8 ∙ share

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Deep Network Interpolation for Accelerated Parallel MR Image Reconstruction

We present a deep network interpolation strategy for accelerated paralle...
07/12/2020 ∙ by Chen Qin, et al. ∙ 1 ∙ share

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3D Probabilistic Segmentation and Volumetry from 2D projection images

X-Ray imaging is quick, cheap and useful for front-line care assessment ...
06/23/2020 ∙ by Athanasios Vlontzos, et al. ∙ 22 ∙ share

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Ultrasound Video Summarization using Deep Reinforcement Learning

Video is an essential imaging modality for diagnostics, e.g. in ultrasou...
05/19/2020 ∙ by Tianrui Liu, et al. ∙ 6 ∙ share

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Regression Forest-Based Atlas Localization and Direction Specific Atlas Generation for Pancreas Segmentation

This paper proposes a fully automated atlas-based pancreas segmentation ...
05/07/2020 ∙ by Masahiro Oda, et al. ∙ 6 ∙ share

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Learning Cross-domain Generalizable Features by Representation Disentanglement

Deep learning models exhibit limited generalizability across different d...
02/29/2020 ∙ by Qingjie Meng, et al. ∙ 9 ∙ share

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Σ-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction

Purpose: To systematically investigate the influence of various data con...
12/18/2019 ∙ by Kerstin Hammernik, et al. ∙ 24 ∙ share

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Deep learning for cardiac image segmentation: A review

Deep learning has become the most widely used approach for cardiac image...
11/09/2019 ∙ by Chen Chen, et al. ∙ 95 ∙ share

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Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network

In this work, a novel approach is proposed for joint analysis of high di...
10/07/2019 ∙ by Shihao Jin, et al. ∙ 28 ∙ share

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Data consistency networks for (calibration-less) accelerated parallel MR image reconstruction

We present simple reconstruction networks for multi-coil data by extendi...
09/25/2019 ∙ by Jo Schlemper, et al. ∙ 15 ∙ share

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Intelligent image synthesis to attack a segmentation CNN using adversarial learning

Deep learning approaches based on convolutional neural networks (CNNs) h...
09/24/2019 ∙ by Liang Chen, et al. ∙ 15 ∙ share

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dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance

AUTOMAP is a promising generalized reconstruction approach, however, it ...
09/24/2019 ∙ by Jo Schlemper, et al. ∙ 30 ∙ share

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Model-Based and Data-Driven Strategies in Medical Image Computing

Model-based approaches for image reconstruction, analysis and interpreta...
09/23/2019 ∙ by Daniel Rueckert, et al. ∙ 30 ∙ share

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Flexible Conditional Image Generation of Missing Data with Learned Mental Maps

Real-world settings often do not allow acquisition of high-resolution vo...
08/29/2019 ∙ by Benjamin Hou, et al. ∙ 13 ∙ share

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Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound

One of the biggest challenges for deep learning algorithms in medical im...
08/21/2019 ∙ by Qingjie Meng, et al. ∙ 4 ∙ share

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Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image

Accelerating the acquisition of magnetic resonance imaging (MRI) is a ch...
08/20/2019 ∙ by Chen Qin, et al. ∙ 4 ∙ share

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Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation

In this work, we present a fully automatic method to segment cardiac str...
08/20/2019 ∙ by Chen Chen, et al. ∙ 0 ∙ share

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Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders

Maintaining good cardiac function for as long as possible is a major con...
08/13/2019 ∙ by Esther Puyol-Antón, et al. ∙ 3 ∙ share

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Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images

Cardiac MR image segmentation is essential for the morphological and fun...
07/23/2019 ∙ by Chen Chen, et al. ∙ 5 ∙ share

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k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlations

Dynamic magnetic resonance imaging (MRI) exhibits high correlations in k...
07/22/2019 ∙ by Chen Qin, et al. ∙ 5 ∙ share

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VS-Net: Variable splitting network for accelerated parallel MRI reconstruction

In this work, we propose a deep learning approach for parallel magnetic ...
07/19/2019 ∙ by Jinming Duan, et al. ∙ 2 ∙ share

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Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation

Deep learning models trained on medical images from a source domain (e.g...
07/05/2019 ∙ by Cheng Ouyang, et al. ∙ 7 ∙ share

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Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction

In the recent years, convolutional neural networks have transformed the ...
07/05/2019 ∙ by Wenjia Bai, et al. ∙ 6 ∙ share

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Multiple Landmark Detection using Multi-Agent Reinforcement Learning

The detection of anatomical landmarks is a vital step for medical image ...
06/30/2019 ∙ by Athanasios Vlontzos, et al. ∙ 3 ∙ share

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Explainable Shape Analysis through Deep Hierarchical Generative Models: Application to Cardiac Remodeling

Quantification of anatomical shape changes still relies on scalar global...
06/28/2019 ∙ by Carlo Biffi, et al. ∙ 1 ∙ share

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Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-space

In fully sampled cardiac MR (CMR) acquisitions, motion can lead to corru...
06/12/2019 ∙ by lkay Oksuz, et al. ∙ 3 ∙ share

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Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes

We introduce a novel personalized Gaussian Process Experts (pGPE) model ...
04/19/2019 ∙ by Ognjen Rudovic, et al. ∙ 8 ∙ share

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Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations

We propose a fully unsupervised multi-modal deformable image registratio...
03/22/2019 ∙ by Chen Qin, et al. ∙ 0 ∙ share

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Daniel Rueckert

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