Jeffrey De Fauw
Jeffrey De Fauw
DeepMind
Bestätigte E-Mail-Adresse bei google.com - Startseite
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Zitiert von
Jahr
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, 2018
7522018
Lasagne: first release
S Dieleman, J Schlüter, C Raffel, E Olson, SK Sønderby, D Nouri, ...
Zenodo: Geneva, Switzerland 3, 2015
361*2015
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
2922020
Data-Efficient Image Recognition with Contrastive Predictive Coding
OJ Hénaff, A Srinivas, J De Fauw, A Razavi, C Doersch, SM Eslami, ...
arXiv preprint arXiv:1905.09272, 2019
2042019
Exploiting cyclic symmetry in convolutional neural networks
S Dieleman, J De Fauw, K Kavukcuoglu
arXiv preprint arXiv:1602.02660, 2016
1942016
A probabilistic u-net for segmentation of ambiguous images
S Kohl, B Romera-Paredes, C Meyer, J De Fauw, JR Ledsam, ...
Advances in neural information processing systems 31, 6965-6975, 2018
1392018
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
792018
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
372016
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
202016
Hierarchical autoregressive image models with auxiliary decoders
J De Fauw, S Dieleman, K Simonyan
arXiv preprint arXiv:1903.04933, 2019
132019
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
112020
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, 1-2, 2020
92020
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
Self-supervised multimodal versatile networks
JB Alayrac, A Recasens, R Schneider, R Arandjelović, J Ramapuram, ...
Advances in Neural Information Processing Systems 33, 2020
22020
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
Generalizable medical image analysis using segmentation and classification neural networks
J De Fauw, JR Ledsam, B Romera-Paredes, S Nikolov, N Tomasev, ...
US Patent App. 16/022,170, 2019
2019
Clinically applicable deep learning for diagnosis and referral in retinal optical coherence tomography
J De Fauw, J Ledsam, BR Paredes, SN Nikolov, N Tomašev, SJ Blackwell, ...
2018
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