| 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 | 2520 | 2020 |
| 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 | 2424 | 2018 |
| A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis X Liu, L Faes, AU Kale, SK Wagner, DJ Fu, A Bruynseels, T Mahendiran, ... The lancet digital health 1 (6), e271-e297, 2019 | 1572 | 2019 |
| 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 | 898 | 2019 |
| Effective gene expression prediction from sequence by integrating long-range interactions Ž Avsec, V Agarwal, D Visentin, JR Ledsam, A Grabska-Barwinska, ... Nature methods 18 (10), 1196-1203, 2021 | 666 | 2021 |
| 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 | 608 | 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 | 355 | 2018 |
| 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 | 271 | 2019 |
| 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 | 257 | 2020 |
| Contrastive training for improved out-of-distribution detection J Winkens, R Bunel, AG Roy, R Stanforth, V Natarajan, JR Ledsam, ... arXiv preprint arXiv:2007.05566, 2020 | 245 | 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 | 197 | 2021 |
| Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer M Kosmin, J Ledsam, B Romera-Paredes, R Mendes, S Moinuddin, ... Radiotherapy and Oncology 135, 130-140, 2019 | 127 | 2019 |
| Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning AV Varadarajan, P Bavishi, P Ruamviboonsuk, P Chotcomwongse, ... Nature communications 11 (1), 130, 2020 | 124 | 2020 |
| Assessing liver function using dynamic Gd‐EOB‐DTPA‐enhanced MRI with a standard 5‐phase imaging protocol K Saito, J Ledsam, S Sourbron, J Otaka, Y Araki, S Akata, K Tokuuye Journal of Magnetic Resonance Imaging 37 (5), 1109-1114, 2013 | 79 | 2013 |
| Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records N Tomašev, N Harris, S Baur, A Mottram, X Glorot, JW Rae, M Zielinski, ... Nature Protocols 16 (6), 2765-2787, 2021 | 74 | 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 | 63 | 2016 |
| Measuring hepatic functional reserve using low temporal resolution Gd-EOB-DTPA dynamic contrast-enhanced MRI: a preliminary study comparing galactosyl human serum albumin … K Saito, J Ledsam, S Sourbron, T Hashimoto, Y Araki, S Akata, K Tokuuye European radiology 24, 112-119, 2014 | 60 | 2014 |
| 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 | 52 | 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 | 29 | 2016 |
| A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan SÖ Arık, J Shor, R Sinha, J Yoon, JR Ledsam, LT Le, MW Dusenberry, ... NPJ digital medicine 4 (1), 146, 2021 | 21 | 2021 |
