Jo Schlemper

发表

Cardiac MR Segmentation from Undersampled k-space Using Deep Latent Representation Learning

Daniel C. Castro, Daniel Rueckert, Joseph V. Hajnal, 2018, MICCAI.

Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach

Ghalib A. Bello, D. Rueckert, D. O’Regan, 2018, IEEE Transactions on Medical Imaging.

ContraReg: Contrastive Learning of Multi-modality Unsupervised Deformable Image Registration

G. Gerig, Jo Schlemper, M. Sofka, 2022, International Conference on Medical Image Computing and Computer-Assisted Intervention.

Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation

D. Rueckert, Jo Schlemper, Wenjia Bai, 2019, STACOM@MICCAI.

Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation

D. Rueckert, Jo Schlemper, Wenjia Bai, 2019, STACOM@MICCAI.

Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge

Daniel Rueckert, Mariya Doneva, Vishal M. Patel, 2021, MLMIR@MICCAI.

Dual-domain self-supervised learning for accelerated non-Cartesian MRI reconstruction

Seyed Sadegh Mohseni Salehi, J. Duncan, J. Duncan, 2022, Medical Image Anal..

DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction

J. Duncan, Jo Schlemper, Chi Liu, 2022, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

Attention U-Net: Learning Where to Look for the Pancreas pdf

Loïc Le Folgoc, Ben Glocker, Daniel Rueckert, 2018, ArXiv.

Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images

Ben Glocker, Daniel Rueckert, Bernhard Kainz, 2018, Medical Image Anal..

Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction

Daniel Rueckert, Joseph V. Hajnal, Anthony N. Price, 2017, IEEE Transactions on Medical Imaging.

A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction

Daniel Rueckert, Joseph V. Hajnal, Anthony N. Price, 2017, IEEE Transactions on Medical Imaging.

A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction

Daniel Rueckert, Joseph V. Hajnal, Anthony N. Price, 2017, IPMI.

Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences

Daniel Rueckert, Chen Qin, Wenjia Bai, 2018, MICCAI.

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

Ronald M. Summers, Daniel Rueckert, Chen Qin, 2019, ArXiv.

Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach

Daniel Rueckert, Jo Schlemper, Timothy J. W. Dawes, 2018, IEEE Transactions on Medical Imaging.

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

Daniel Rueckert, Declan P. O'Regan, Chen Qin, 2019, MICCAI.

Attention-Gated Networks for Improving Ultrasound Scan Plane Detection pdf

Liang Chen, Ben Glocker, Daniel Rueckert, 2018, MICCAI 2018.

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

Daniel Rueckert, Joseph V. Hajnal, Chen Qin, 2019, ArXiv.

Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI

Guang Yang, Daniel Rueckert, Pedro Ferreira, 2018, MICCAI.

Point-of-Care MRI with Artificial Intelligence to Measure Midline Shift in Acute Stroke Follow-Up

Seyed Sadegh Mohseni Salehi, Matthew M. Yuen, J. Rothberg, 2022, medRxiv.

Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction

Daniel Rueckert, Jo Schlemper, Michal Sofka, 2019, MICCAI.

Exploiting Motion for Deep Learning Reconstruction of Extremely-Undersampled Dynamic MRI

Daniel Rueckert, Joseph V. Hajnal, Chen Qin, 2019, MICCAI.

Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction pdf

Guang Yang, Daniel Rueckert, Andreas K. Maier, 2018, MICCAI.

Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity‐weighted coil combination

D. Rueckert, Jo Schlemper, J. Duan, 2021, Magnetic resonance in medicine.

Σ-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction pdf

Ronald M. Summers, Chen Qin, Jo Schlemper, 2019, ArXiv.

Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction

Seyed Sadegh Mohseni Salehi, J. Duncan, Jo Schlemper, 2023, ArXiv.

dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance pdf

Daniel Rueckert, Joseph V. Hajnal, Jinming Duan, 2019, ArXiv.

Bayesian Deep Learning for Accelerated MR Image Reconstruction

Daniel C. Castro, Daniel Rueckert, Joseph V. Hajnal, 2018, MLMIR@MICCAI.

Complementary time‐frequency domain networks for dynamic parallel MR image reconstruction

D. Rueckert, J. Hajnal, Jo Schlemper, 2020, Magnetic resonance in medicine.

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

Daniel Rueckert, Chen Qin, Jo Schlemper, 2019, MICCAI.

Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image pdf

Daniel Rueckert, Chen Qin, Wenjia Bai, 2018, MLMIR@MICCAI.

Combining Deep Learning and Shape Priors for Bi-Ventricular Segmentation of Volumetric Cardiac Magnetic Resonance Images

Daniel Rueckert, Declan P. O'Regan, Wenjia Bai, 2018, ShapeMI@MICCAI.

Deep nested level sets: Fully automated segmentation of cardiac MR images in patients with pulmonary hypertension pdf

Daniel Rueckert, Declan P. O'Regan, Wenjia Bai, 2018, MICCAI.

Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training

Loïc Le Folgoc, Daniel Rueckert, Chen Qin, 2019, STACOM@MICCAI.

A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction

D. Rueckert, Jose Caballero, J. Hajnal, 2017, IEEE Transactions on Medical Imaging.

Automatic 3D bi-ventricular segmentation of cardiac images by a shape-constrained multi-task deep learning approach

Ghalib A. Bello, D. Rueckert, D. O’Regan, 2018, ArXiv.