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

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. ∙ 23 ∙ 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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3D High-Resolution Cardiac Segmentation Reconstruction from 2D Views using Conditional Variational Autoencoders

Accurate segmentation of heart structures imaged by cardiac MR is key fo...
02/28/2019 ∙ by Carlo Biffi, et al. ∙ 4 ∙ share

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Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study

Background: The trend towards large-scale studies including population i...
01/27/2019 ∙ by Robert Robinson, et al. ∙ 38 ∙ share

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Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review

The medical image analysis field has traditionally been focused on the d...
12/20/2018 ∙ by Juan J. Cerrolaza, et al. ∙ 6 ∙ share

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GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation

Medical imaging is a domain which suffers from a paucity of manually ann...
11/26/2018 ∙ by Christopher Bowles, et al. ∙ 0 ∙ share

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Weakly Supervised Estimation of Shadow Confidence Maps in Ultrasound Imaging

Detecting acoustic shadows in ultrasound images is important in many cli...
11/20/2018 ∙ by Qingjie Meng, et al. ∙ 20 ∙ share

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A generic framework for privacy preserving deep learning

We detail a new framework for privacy preserving deep learning and discu...
11/09/2018 ∙ by Theo Ryffel, et al. ∙ 12 ∙ share

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Multi-Task Learning for Left Atrial Segmentation on GE-MRI

Segmentation of the left atrium (LA) is crucial for assessing its anatom...
10/31/2018 ∙ by Chen Chen, et al. ∙ 4 ∙ share

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Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning

Good quality of medical images is a prerequisite for the success of subs...
10/29/2018 ∙ by lkay Oksuz, et al. ∙ 2 ∙ share

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GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks

One of the biggest issues facing the use of machine learning in medical ...
10/25/2018 ∙ by Christopher Bowles, et al. ∙ 4 ∙ share

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Daniel Rueckert

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