Joseph Ledsam
Joseph Ledsam
Research Scientist, DeepMind
Verified email at
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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, ...
Buffers for electrophoresis and use thereof
KJ Hacker, KO Voss
US Patent 7,282,128, 2007
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
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
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
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
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