| Electrohydrodynamic preparation of particles, capsules and bubbles for biomedical engineering applications M Enayati, MW Chang, F Bragman, M Edirisinghe, E Stride Colloids and Surfaces A: Physicochemical and Engineering Aspects 382 (1-3 …, 2011 | 162 | 2011 |
| Improving data augmentation for medical image segmentation Z Eaton-Rosen, F Bragman, S Ourselin, MJ Cardoso | 117 | 2018 |
| Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions Z Eaton-Rosen, F Bragman, S Bisdas, S Ourselin, MJ Cardoso Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 92 | 2018 |
| Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels FJS Bragman*, R Tanno*, S Ourselin, DC Alexander, MJ Cardoso Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2019 | 85 | 2019 |
| Disease Progression Modelling in Chronic Obstructive Pulmonary Disease (COPD) AL Young*, FJS Bragman*, B Rangelov, ML Han, CJ Galbán, DA Lynch, ... American Journal of Respiratory and Critical Care Medicine 201 (3), 2020 | 76 | 2020 |
| Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning FJS Bragman, R Tanno, Z Eaton-Rosen, W Li, DJ Hawkes, S Ourselin, ... International Conference on Medical Image Computing and Computer-Assisted …, 2018 | 63* | 2018 |
| Pulmonary lobe segmentation with probabilistic segmentation of the fissures and a groupwise fissure prior FJS Bragman, JR McClelland, J Jacob, JR Hurst, DJ Hawkes IEEE transactions on medical imaging 36 (8), 1650-1663, 2017 | 43 | 2017 |
| Hot electrospinning of polyurethane fibres M Nangrejo, F Bragman, Z Ahmad, E Stride, M Edirisinghe Materials letters 68, 482-485, 2012 | 19 | 2012 |
| Applying Artificial Intelligence Methods for the Estimation of Disease Incidence: the Utility of Language Models Y Zhang, R Walecki, J Winter, F Bragman, S Lourenco, C Hart, A Baker, ... Frontiers in Digital Health 2, 31, 2020 | 7 | 2020 |
| Improving Data Augmentation for Medical Image Segmentation. 2018 Z Eaton-Rosen, F Bragman, S Ourselin, MJ Cardoso URL: https://openreview. net/pdf, 2018 | 7 | 2018 |
| A spatio-temporal network for video semantic segmentation in surgical videos M Grammatikopoulou, R Sanchez-Matilla, F Bragman, D Owen, ... International Journal of Computer Assisted Radiology and Surgery 19 (2), 375-382, 2024 | 4 | 2024 |
| Multi-scale analysis of imaging features and its use in the study of COPD exacerbation susceptible phenotypes FJS Bragman, JR McClelland, M Modat, S Ourselin, JR Hurst, DJ Hawkes Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th …, 2014 | 3 | 2014 |
| How to control the learning rate of adaptive sampling schemes L Berger, E Hyde, N Pavithran, F Mumtaz, F Bragman, MJ Cardoso, ... | 2 | 2022 |
| Disease progression patterns in COPD FJS Bragman, AL Young, DJ Hawkes, DC Alexander, JR Hurst European Respiratory Journal 52 (suppl 62), 2018 | 2 | 2018 |
| Manifold Learning of COPD F Bragman, J McClelland, J Jacob, J Hurst, D Hawkes International Conference on Medical Image Computing and Computer-Assisted …, 2017 | 2 | 2017 |
| Learning Task-Specific and Shared Representations in Medical Imaging FJS Bragman*, R Tanno*, S Ourselin, DC Alexander, MJ Cardoso International Conference on Medical Image Computing and Computer-Assisted …, 2019 | | 2019 |
| Disease Progression Modelling in AL Young, FJS Bragman, B Rangelov, ML Han, CJ Galbán, DA Lynch, ... | | 2019 |
| Quantitative lung CT analysis for the study and diagnosis of Chronic Obstructive Pulmonary Disease FJS Bragman University of London, University College London (United Kingdom), 2018 | | 2018 |
| Multi-scale analysis of imaging features to study exacerbation susceptible COPD FJS Bragman, JR McClelland, DJ Hawkes, JR Hurst European Respiratory Journal 44 (Suppl 58), 2014 | | 2014 |
Sebastien OurselinProfessor of Healthcare Engineering, King's College London在 kcl.ac.uk 的電子郵件地址已通過驗證
