| Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations CH Sudre, W Li, T Vercauteren, S Ourselin, MJ Cardoso Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2017 | 1576 | 2017 |
| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 1211 | 2018 |
| The future of digital health with federated learning N Rieke, J Hancox, W Li, F Milletari, HR Roth, S Albarqouni, S Bakas, ... NPJ digital medicine 3 (1), 119, 2020 | 675 | 2020 |
| Interactive medical image segmentation using deep learning with image-specific fine-tuning G Wang, W Li, MA Zuluaga, R Pratt, PA Patel, M Aertsen, T Doel, ... IEEE Transactions on Medical Imaging, 2018 | 615 | 2018 |
| NiftyNet: a deep-learning platform for medical imaging E Gibson, W Li, C Sudre, L Fidon, DI Shakir, G Wang, Z Eaton-Rosen, ... Computer methods and programs in biomedicine 158, 113-122, 2018 | 556 | 2018 |
| Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks G Wang, W Li, S Ourselin, T Vercauteren Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2018 | 466 | 2018 |
| Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks G Wang, W Li, M Aertsen, J Deprest, S Ourselin, T Vercauteren Neurocomputing 338, 34-45, 2019 | 320 | 2019 |
| On the compactness, efficiency, and representation of 3D convolutional networks: brain parcellation as a pretext task W Li, G Wang, L Fidon, S Ourselin, MJ Cardoso, T Vercauteren Information Processing in Medical Imaging: 25th International Conference …, 2017 | 305 | 2017 |
| Privacy-preserving federated brain tumour segmentation W Li, F Milletarì, D Xu, N Rieke, J Hancox, W Zhu, M Baust, Y Cheng, ... Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019 …, 2019 | 303 | 2019 |
| Weakly-supervised convolutional neural networks for multimodal image registration Y Hu, M Modat, E Gibson, W Li, N Ghavami, E Bonmati, G Wang, ... Medical image analysis 49, 1-13, 2018 | 300 | 2018 |
| DeepIGeoS: a deep interactive geodesic framework for medical image segmentation G Wang, MA Zuluaga, W Li, R Pratt, PA Patel, M Aertsen, T Doel, ... IEEE transactions on pattern analysis and machine intelligence 41 (7), 1559-1572, 2018 | 294 | 2018 |
| Automatic brain tumor segmentation based on cascaded convolutional neural networks with uncertainty estimation G Wang, W Li, S Ourselin, T Vercauteren Frontiers in computational neuroscience 13, 56, 2019 | 152 | 2019 |
| Generalised wasserstein dice score for imbalanced multi-class segmentation using holistic convolutional networks L Fidon, W Li, LC Garcia-Peraza-Herrera, J Ekanayake, N Kitchen, ... International MICCAI Brainlesion Workshop, 64-76, 2017 | 145 | 2017 |
| An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI M Ebner, G Wang, W Li, M Aertsen, PA Patel, R Aughwane, A Melbourne, ... NeuroImage 206, 116324, 2020 | 123 | 2020 |
| Toolnet: holistically-nested real-time segmentation of robotic surgical tools LC Garcia-Peraza-Herrera, W Li, L Fidon, C Gruijthuijsen, A Devreker, ... 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2017 | 119 | 2017 |
| Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan D Yang, Z Xu, W Li, A Myronenko, HR Roth, S Harmon, S Xu, B Turkbey, ... Medical Image Analysis, 101992, 2021 | 116 | 2021 |
| Automatic brain tumor segmentation using convolutional neural networks with test-time augmentation G Wang, W Li, S Ourselin, T Vercauteren Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2019 | 107 | 2019 |
| Real-Time Segmentation of Non-rigid Surgical Tools Based on Deep Learning and Tracking LC García-Peraza-Herrera, W Li, C Gruijthuijsen, A Devreker, G Attilakos, ... International Workshop on Computer-Assisted and Robotic Endoscopy, 84-95, 2016 | 95 | 2016 |
| Federated learning for breast density classification: A real-world implementation HR Roth, K Chang, P Singh, N Neumark, W Li, V Gupta, S Gupta, L Qu, ... Domain Adaptation and Representation Transfer, and Distributed and …, 2020 | 88 | 2020 |
| 2017 Robotic Instrument Segmentation Challenge M Allan, A Shvets, T Kurmann, Z Zhang, R Duggal, YH Su, N Rieke, ... arXiv preprint arXiv:1902.06426, 2019 | 88 | 2019 |
Tom VercauterenProfessor of Interventional Image Computing, King's College LondonVerified email at kcl.ac.uk
