Trevor Back
Trevor Back
DeepMind
Verified email at deepmind.com
Title
Cited by
Cited by
Year
The kinetics human action video dataset
W Kay, J Carreira, K Simonyan, B Zhang, C Hillier, S Vijayanarasimhan, ...
arXiv preprint arXiv:1705.06950, 2017
7252017
Clinically applicable deep learning for diagnosis and referral in retinal disease
J De Fauw, JR Ledsam, B Romera-Paredes, S Nikolov, N Tomasev, ...
Nature medicine 24 (9), 1342-1350, 2018
5282018
A clinically applicable approach to continuous prediction of future acute kidney injury
N Tomašev, X Glorot, JW Rae, M Zielinski, H Askham, A Saraiva, ...
Nature 572 (7767), 116-119, 2019
1112019
International evaluation of an AI system for breast cancer screening
SM McKinney, M Sieniek, V Godbole, J Godwin, N Antropova, H Ashrafian, ...
Nature 577 (7788), 89-94, 2020
832020
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
S Nikolov, S Blackwell, R Mendes, J De Fauw, C Meyer, C Hughes, ...
arXiv preprint arXiv:1809.04430, 2018
512018
Automated analysis of retinal imaging using machine learning techniques for computer vision
J De Fauw, P Keane, N Tomasev, D Visentin, G van den Driessche, ...
F1000Research 5, 2016
342016
Applying machine learning to automated segmentation of head and neck tumour volumes and organs at risk on radiotherapy planning CT and MRI scans
C Chu, J De Fauw, N Tomasev, BR Paredes, C Hughes, J Ledsam, ...
F1000Research 5 (2104), 2104, 2016
162016
Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study
L Faes, SK Wagner, DJ Fu, X Liu, E Korot, JR Ledsam, T Back, R Chopra, ...
The Lancet Digital Health 1 (5), e232-e242, 2019
142019
Service evaluation of the implementation of a digitally-enabled care pathway for the recognition and management of acute kidney injury
A Connell, H Montgomery, S Morris, C Nightingale, S Stanley, M Emerson, ...
F1000Research 6, 2017
132017
The kinetics human action video dataset (2017)
W Kay, J Carreira, K Simonyan, B Zhang, C Hillier, S Vijayanarasimhan, ...
arXiv preprint arXiv:1705.06950, 2017
102017
Evaluation of a digitally-enabled care pathway for acute kidney injury management in hospital emergency admissions
A Connell, H Montgomery, P Martin, C Nightingale, O Sadeghi-Alavijeh, ...
NPJ digital medicine 2 (1), 1-9, 2019
42019
Implementation of a Digitally Enabled Care Pathway (Part 2): Qualitative Analysis of Experiences of Healthcare Professionals
A Connell, G Black, H Montgomery, P Martin, C Nightingale, D King, ...
Journal of medical Internet research 21 (7), e13143, 2019
32019
Implementation of a digitally enabled care pathway (part 1): impact on clinical outcomes and associated health care costs
A Connell, R Raine, P Martin, EC Barbosa, S Morris, C Nightingale, ...
Journal of medical Internet research 21 (7), e13147, 2019
32019
Predicting conversion to wet age-related macular degeneration using deep learning
J Yim, R Chopra, T Spitz, J Winkens, A Obika, C Kelly, H Askham, M Lukic, ...
Nature Medicine, 1-8, 2020
2020
Prediction of future adverse health events using neural networks by pre-processing input sequences to include presence features
N Tomasev, X Glorot, JW Rae, M Zielinski, A Mottram, H Askham, ...
US Patent App. 16/683,139, 2020
2020
Author Correction: Unveiling the predictive power of static structure in glassy systems
V Bapst, T Keck, A Grabska-Barwińska, C Donner, ED Cubuk, ...
Nature Physics, 1-1, 2020
2020
Unveiling the predictive power of static structure in glassy systems
V Bapst, T Keck, A Grabska-Barwińska, C Donner, ED Cubuk, ...
Nature Physics 16 (4), 448-454, 2020
2020
3-d convolutional neural networks for organ segmentation in medical images for radiotherapy planning
S Nikolov, S Blackwell, J De Fauw, B Romera-Paredes, C Meyer, ...
US Patent App. 16/565,384, 2020
2020
International evaluation of an AI system for breast cancer screening
F Gilbert, S Mayer Mckinney, M Sieniek, V Godbole, J Godwin, ...
Springer Nature, 2019
2019
Developing Deep Learning Continuous Risk Models for Early Adverse Event Prediction in Electronic Health Records: an AKI Case Study
N Tomašev, JR Ledsam, X Glorot, JW Rae, M Zielinski, H Askham, ...
2019
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