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 | 1419 | 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 | 1110 | 2020 |
Data-Efficient Image Recognition with Contrastive Predictive Coding OJ Hénaff, A Srinivas, JD Fauw, A Razavi, C Doersch, SMA Eslami, ... | 721 | 2020 |
Lasagne: first release S Dieleman, J Schlüter, C Raffel, E Olson, SK Sønderby, D Nouri, ... Zenodo: Geneva, Switzerland 3, 74, 2015 | 424* | 2015 |
Exploiting cyclic symmetry in convolutional neural networks S Dieleman, J De Fauw, K Kavukcuoglu International conference on machine learning, 1889-1898, 2016 | 300 | 2016 |
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 | 288 | 2018 |
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 | 187 | 2018 |
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 | 129 | 2020 |
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 | 101 | 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 | 96 | 2020 |
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 | 51 | 2016 |
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 | 27 | 2021 |
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 | 25 | 2016 |
Hierarchical autoregressive image models with auxiliary decoders J De Fauw, S Dieleman, K Simonyan arXiv preprint arXiv:1903.04933, 2019 | 22 | 2019 |
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 | 19 | 2019 |
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 | 12 | 2021 |
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 | 1 | 2021 |
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 | 1 | 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 |