Jeffrey De Fauw
Jeffrey De Fauw
Unknown affiliation
Verified email at - Homepage
Cited by
Cited by
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
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
Data-Efficient Image Recognition with Contrastive Predictive Coding
OJ Hénaff, A Srinivas, JD Fauw, A Razavi, C Doersch, SMA Eslami, ...
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, 2018
Lasagne: first release
S Dieleman, J Schlüter, C Raffel, E Olson, SK Sønderby, D Nouri, ...
Zenodo: Geneva, Switzerland 3, 74, 2015
Exploiting cyclic symmetry in convolutional neural networks
S Dieleman, J De Fauw, K Kavukcuoglu
International conference on machine learning, 1889-1898, 2016
Self-supervised multimodal versatile networks
JB Alayrac, A Recasens, R Schneider, R Arandjelović, J Ramapuram, ...
Advances in Neural Information Processing Systems 33, 25-37, 2020
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
S Nikolov, S Blackwell, A Zverovitch, R Mendes, M Livne, J De Fauw, ...
arXiv preprint arXiv:1809.04430, 2018
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
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
Clinically applicable segmentation of head and neck anatomy for radiotherapy: deep learning algorithm development and validation study
S Nikolov, S Blackwell, A Zverovitch, R Mendes, M Livne, J De Fauw, ...
Journal of medical Internet research 23 (7), e26151, 2021
Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol
V Sounderajah, H Ashrafian, RM Golub, S Shetty, J De Fauw, L Hooft, ...
BMJ open 11 (6), e047709, 2021
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
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
Hierarchical autoregressive image models with auxiliary decoders
J De Fauw, S Dieleman, K Simonyan
arXiv preprint arXiv:1903.04933, 2019
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, 2016
Lasagne: First release.(2015)
S Dieleman, J Schlüter, C Raffel, E Olson, SK Sønderby, D Nouri, ...
URL http://dx. doi. org/10.5281/zenodo 27878, 2015
3-D convolutional neural networks for organ segmentation in medical images for radiotherapy planning
S Nikolov, S Blackwell, J De Fauw, B Romera-Paredes, CL Meyer, ...
US Patent 11,100,647, 2021
Rahmenbedingungen zur Sammlung von „Real-Life “-Daten am Beispiel der Augenklinik der Universität München
K Kortüm, C Kern, G Meyer, S Priglinger, C Hirneiß
Klinische Monatsblätter für Augenheilkunde 234 (12), 1477-1482, 2017
System and Method for Interpretation of Multiple Medical Images Using Deep Learning
S McKinney, M Sieniek, V Godbole, S Shetty, N Antropova, J Godwin, ...
US Patent App. 17/597,876, 2022
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