Sébastien Ourselin

Computer Vision and Pattern Recognition · 2000–2022

A researcher in the Science corpus: 39 papers; trusts 485 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 364 citations into it; #7,871 of 21,575 by trust here, #2,536 by citations
• Artificial Intelligence — 240 citations into it
• Radiology, Nuclear Medicine and Imaging — 129 citations into it; #2,864 of 3,265 by trust here, #223 by citations
• Model Architectures — 68 citations into it
• Biomedical Engineering — 66 citations into it
• Neurology — 43 citations into it
• Optimization & Training — 43 citations into it
• Biophysics — 42 citations into it

Whom Sébastien Ourselin trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Sergey Ioffe
6. Yoshua Bengio
7. Karen Simonyan
8. Christian Szegedy
9. Yann LeCun
10. X. Zhang

Who trusts Sébastien Ourselin overall

1. Alexis Roche
2. Vaidy Mani
3. Josien P. W. Pluim
4. Marc Modat
5. Nicholas Ayache
6. Metin Nafi Gurcan
7. Guotai Wang
8. Xavier Pennec
9. Dr.S. rivazhagan
10. M. Jorge Cardoso

Papers from Sébastien Ourselin's perspective

Ordered by how strongly the researchers Sébastien Ourselin trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2017) — 476 citations
• The Medical Segmentation Decathlon (2021) — 1,513 citations
• Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks (2017) — 546 citations
• Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations (2017) — 2,736 citations
• The future of digital health with federated learning (2020) — 2,960 citations
• Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge (2011) — 492 citations
• Weakly-supervised convolutional neural networks for multimodal image registration (2018) — 442 citations
• Longitudinal multiple sclerosis lesion segmentation: Resource and challenge (2017) — 383 citations
• Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge (2019) — 346 citations
• Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks (2018) — 730 citations
• Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography (2017) — 328 citations
• Reduced acquisition time PET pharmacokinetic modelling using simultaneous ASL–MRI: proof of concept (2018) — 14 citations
• Symptom prevalence, duration, and risk of hospital admission in individuals infected with SARS-CoV-2 during periods of omicron and delta variant dominance: a prospective observational study from the ZOE COVID Study (2022) — 714 citations
• MONAI: An open-source framework for deep learning in healthcare (2022) — 996 citations
• Brain Imaging Generation with Latent Diffusion Models (2022) — 470 citations
• Vaccine side-effects and SARS-CoV-2 infection after vaccination in users of the COVID Symptom Study app in the UK: a prospective observational study (2021) — 866 citations
• Attributes and predictors of long COVID (2021) — 2,181 citations
• TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning (2020) — 558 citations
• CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation (2020) — 655 citations
• Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study (2020) — 1,662 citations