Следене
Joseph Ledsam
Joseph Ledsam
Research Scientist, Google
Потвърден имейл адрес: google.com
Заглавие
Позовавания
Позовавания
Година
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
17232018
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
14732020
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
8682019
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
6062019
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
3822018
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
2382018
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
1822021
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
1552019
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
1452020
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
1312020
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
752013
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
722019
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
632021
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
632020
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 (1573), 1573, 2016
572016
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
562014
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
262021
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
262016
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
25*2019
Service evaluation of the implementation of a digitally-enabled care pathway for the recognition and management of acute kidney injury (version 2)
A Connell, H Montgomery, S Morris, C Nightingale, S Stanley, M Emerson, ...
F1000Res 6, 2017
192017
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