Joseph Ledsam
Joseph Ledsam
Research Scientist, DeepMind
Verified email at google.com
TitleCited byYear
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
3832018
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
602013
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, 6965-6975, 2018
552018
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
462019
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
342014
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
332018
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
292019
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
292016
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
142016
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
122017
Modeling DCE‐MRI at low temporal resolution: A case study on rheumatoid arthritis
JR Ledsam, R Hodgson, RJ Moots, SP Sourbron
Journal of Magnetic Resonance Imaging 38 (6), 1554-1563, 2013
72013
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
52020
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
52019
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
32019
Deep learning under scrutiny: performance against health care professionals in detecting diseases from medical imaging-systematic review and meta-Analysis
L Faes, X Liu, A Kale, A Bruynseels, M Shamdas, G Moraes, DJ Fu, ...
32019
Buffers for electrophoresis and use thereof
KJ Hacker, KO Voss
US Patent 7,282,128, 2007
32007
DCE-MRI for Early Prediction of Response in Hepatocellular Carcinoma after TACE and Sorafenib Therapy: A Pilot Study
K Saito, J Ledsam, K Sugimoto, S Sourbron, Y Araki, K Tokuuye
Journal of the Belgian Society of Radiology 102 (1), 2018
22018
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
12020
Automated Development of Deep Learning Models to Diagnose Retinal Disease from Fundus and Optical Coherence Tomography Images
PA Keane, L Faes, S Wagner, DJ Fu, JR Ledsam, R Chopra, C Kern, ...
Investigative Ophthalmology & Visual Science 60 (9), 1453-1453, 2019
2019
Diagnostic accuracy and interobserver variability of macular disease evaluation using optical coherence tomography
SK Wagner, R Chopra, JR Ledsam, H Askham, S Blackwell, L Faes, ...
Investigative Ophthalmology & Visual Science 60 (9), 1849-1849, 2019
2019
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Articles 1–20