Jeffrey De Fauw
Jeffrey De Fauw
DeepMind
Verified email at mathbb.com - Homepage
Title
Cited by
Cited by
Year
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
8852018
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
4122020
Lasagne: first release
S Dieleman, J Schlüter, C Raffel, E Olson, SK Sønderby, D Nouri, ...
Zenodo: Geneva, Switzerland 3, 74, 2015
398*2015
Data-Efficient Image Recognition with Contrastive Predictive Coding
OJ Hénaff, A Srinivas, JD Fauw, A Razavi, C Doersch, SMA Eslami, ...
2782020
Exploiting cyclic symmetry in convolutional neural networks
S Dieleman, J De Fauw, K Kavukcuoglu
International conference on machine learning, 1889-1898, 2016
2182016
A probabilistic u-net for segmentation of ambiguous images
SAA Kohl, B Romera-Paredes, C Meyer, J De Fauw, JR Ledsam, ...
arXiv preprint arXiv:1806.05034, 2018
1622018
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
1102018
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
412016
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
212016
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 26 (6), 892-899, 2020
202020
Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering Group
V Sounderajah, H Ashrafian, R Aggarwal, J De Fauw, AK Denniston, ...
Nature medicine 26 (6), 807-808, 2020
152020
Hierarchical autoregressive image models with auxiliary decoders
J De Fauw, S Dieleman, K Simonyan
arXiv preprint arXiv:1903.04933, 2019
152019
Generalizable medical image analysis using segmentation and classification neural networks
J De Fauw, JR Ledsam, B Romera-Paredes, S Nikolov, N Tomasev, ...
US Patent 10,198,832, 2019
62019
Self-supervised multimodal versatile networks
JB Alayrac, A Recasens, R Schneider, R Arandjelović, J Ramapuram, ...
arXiv preprint arXiv:2006.16228, 2020
32020
Addendum: International evaluation of an AI system for breast cancer screening
SM McKinney, M Sieniek, V Godbole, J Godwin, N Antropova, H Ashrafian, ...
Nature 586 (7829), E19-E19, 2020
22020
Generalizable medical image analysis using segmentation and classification neural networks
J De Fauw, JR Ledsam, B Romera-Paredes, S Nikolov, N Tomasev, ...
US Patent 10,878,601, 2020
2020
Automatic segmentation using artificial intelligence of baseline anatomical parameters of patients starting anti-VEGF injections for neovascular age-related macular degeneration
G Moraes, R Chopra, DJ Fu, S Wagner, T Spitz, M Wilson, J Yim, ...
Investigative Ophthalmology & Visual Science 61 (7), 1633-1633, 2020
2020
Quantitative analysis of change in retinal tissues in neovascular age-related macular degeneration using artificial intelligence
R Chopra, G Moraes, DJ Fu, S Wagner, T Spitz, M Wilson, J Yim, ...
Investigative Ophthalmology & Visual Science 61 (7), 1152-1152, 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
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Articles 1–19