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Joseph Ledsam
Joseph Ledsam
Research Scientist, Google
Verified email at google.com
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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
14682018
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
11662020
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
6792019
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
5042019
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
2932018
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
2012018
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
1292019
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
1072020
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
872020
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
792021
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
692013
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
582019
A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit …
X Liu, L Faes, AU Kale, SK Wagner, DJ Fu, A Bruynseels, T Mahendiran, ...
Artificial Intelligence in Lung Cancer Pathology Images, 2018
582018
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
532016
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 (1), 112-119, 2014
532014
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), 1-8, 2020
482020
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
322021
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
272016
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
22*2019
Service evaluation of the implementation of a digitally-enabled care pathway for the recognition and management of acute kidney injury
A Connell, H Montgomery, S Morris, C Nightingale, S Stanley, M Emerson, ...
F1000Research 6, 2017
182017
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