Rueckert, Daniel

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A. P., Bishop, R., Rueckert, D., & Wang, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Image Processing TechniquesAdvanced Neural Network Applications

Cited by 7,570

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

Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., et al. (2018) · arXiv

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 5,458

Nonrigid registration using free-form deformations: application to breast mr images

Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O., & Hawkes, D. J. (1999) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesAdvanced Radiotherapy Techniques

Cited by 5,265

Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 3,498

Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research

Maas, A. I. R., Menon, D. K., Adelson, P. D., Andelic, N., Bell, M. J., Belli, A., Bragge, P., Brazinova, A., Büki, A., Chesnut, R. M., et al. (2017) · The Lancet Neurology

Traumatic Brain Injury ResearchTraumatic Brain Injury and Neurovascular Disturbances

Cited by 2,609

Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration

Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M.-C., Christensen, G. E., Collins, D. L., Gee, J., Hellier, P., et al. (2009) · NeuroImage

Medical Image Segmentation TechniquesAdvanced MRI Techniques and Applications

Cited by 2,227

Attention gated networks: learning to leverage salient regions in medical images

Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., & Rueckert, D. (2019) · Medical Image Analysis

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 2,014

A deep cascade of convolutional neural networks for dynamic MR image reconstruction

Schlemper, J., Caballero, J., Hajnal, J. V., Price, A. N., & Rueckert, D. (2018) · IEEE Transactions on Medical Imaging

Advanced MRI Techniques and ApplicationsSparse and Compressive Sensing Techniques

Cited by 1,338

Evaluation and mitigation of the limitations of large language models in clinical decision-making

Hager, P., Jungmann, F., Holland, R., Bhagat, K., Hubrecht, I., Knauer, M., Vielhauer, J., Makowski, M., Braren, R., Kaissis, G., et al. (2024) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 798

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Bai, W., Sinclair, M., Tarroni, G., Oktay, O., Rajchl, M., Vaillant, G., Lee, A. M., Aung, N., Lukaschuk, E., Sanghvi, M. M., et al. (2018) · Journal of Cardiovascular Magnetic Resonance

Medical Image Segmentation TechniquesCardiac Imaging and Diagnostics

Cited by 773

Anatomically constrained neural networks (ACNNS): application to cardiac image enhancement and segmentation

Oktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, W., Caballero, J., Cook, S. A., de Marvao, A., Dawes, T., O'Regan, D. P., et al. (2018) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 751

Self-supervised learning for medical image analysis using image context restoration

Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., & Rueckert, D. (2019) · Medical Image Analysis

Domain Adaptation and Few-Shot LearningMedical Image Segmentation Techniques

Cited by 575

End-to-end privacy preserving deep learning on multi-institutional medical imaging

Kaissis, G., Ziller, A., Passerat-Palmbach, J., Ryffel, T., Usynin, D., Trask, A., Lima, I., Mancuso, J., Jungmann, F., Steinborn, M.-M., et al. (2021) · Nature Machine Intelligence

Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and Education

Cited by 513

SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound

Baumgartner, C. F., Kamnitsas, K., Matthew, J., Fletcher, T. P., Smith, S., Koch, L. M., Kainz, B., & Rueckert, D. (2017) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Cited by 416

DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks

Rajchl, M., Lee, M. C. H., Oktay, O., Kamnitsas, K., Passerat-Palmbach, J., Bai, W., Damodaram, M., Rutherford, M. A., Hajnal, J. V., Kainz, B., et al. (2017) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 410

Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex

Arslan, S., Ktena, S. I., Makropoulos, A., Robinson, E. C., Rueckert, D., & Parisot, S. (2018) · NeuroImage

Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and Applications

Cited by 374

Rich-club organization of the newborn human brain

Ball, G., Aljabar, P., Zebari, S., Tusor, N., Arichi, T., Merchant, N., Robinson, E. C., Ogundipe, E., Rueckert, D., Edwards, A. D., et al. (2014) · Proceedings of the National Academy of Sciences

Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and Applications

Cited by 360

DeepMedic for Brain Tumor Segmentation

Kamnitsas, K., Ferrante, E., Parisot, S., Ledig, C., Nori, A. V., Criminisi, A., Rueckert, D., & Glocker, B. (2016) · Lecture Notes in Computer Science

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 346

A generic framework for privacy preserving deep learning

Ryffel, T., Trask, A., Dahl, M., Wagner, B., Mancuso, J., Rueckert, D., & Passerat-Palmbach, J. (2018) · arXiv

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 335

Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

Dou, Q., So, T. Y., Jiang, M., Liu, Q., Vardhanabhuti, V., Kaissis, G., Li, Z., Si, W., Lee, H. H. C., Yu, K., et al. (2021) · npj Digital Medicine

COVID-19 diagnosis using AIPrivacy-Preserving Technologies in Data

Cited by 278

Multi-input Cardiac Image Super-Resolution Using Convolutional Neural Networks

Oktay, O., Bai, W., Lee, M., Guerrero, R., Kamnitsas, K., Caballero, J., de Marvao, A., Cook, S., O’Regan, D., & Rueckert, D. (2016) · Lecture Notes in Computer Science

Advanced Image Processing TechniquesMedical Image Segmentation Techniques

Cited by 246

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

Duan, J., Bello, G., Schlemper, J., Bai, W., Dawes, T. J. W., Biffi, C., de Marvao, A., Doumoud, G., O'Regan, D. P., & Rueckert, D. (2019) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesCardiac Imaging and Diagnostics

Cited by 227

Fast Volume Reconstruction From Motion Corrupted Stacks of 2D Slices

Kainz, B., Steinberger, M., Wein, W., Kuklisova-Murgasova, M., Malamateniou, C., Keraudren, K., Torsney-Weir, T., Rutherford, M., Aljabar, P., Hajnal, J. V., et al. (2015) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesMedical Imaging Techniques and Applications

Cited by 200

Differential diagnosis of neurodegenerative diseases using structural MRI data

Koikkalainen, J., Rhodius-Meester, H., Tolonen, A., Barkhof, F., Tijms, B., Lemstra, A. W., Tong, T., Guerrero, R., Schuh, A., Ledig, C., et al. (2016) · NeuroImage: Clinical

Dementia and Cognitive Impairment ResearchArtificial Intelligence in Healthcare

Cited by 193