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Dale R Webster
Dale R Webster
Research Director, Google
Verified email at google.com
Title
Cited by
Cited by
Year
Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
V Gulshan, L Peng, M Coram, MC Stumpe, D Wu, A Narayanaswamy, ...
jama 316 (22), 2402-2410, 2016
74852016
Large language models encode clinical knowledge
K Singhal, S Azizi, T Tu, SS Mahdavi, J Wei, HW Chung, N Scales, ...
Nature 620 (7972), 172-180, 2023
20362023
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
R Poplin, AV Varadarajan, K Blumer, Y Liu, MV McConnell, GS Corrado, ...
Nature biomedical engineering 2 (3), 158-164, 2018
17062018
Direct detection of DNA methylation during single-molecule, real-time sequencing
BA Flusberg, DR Webster, JH Lee, KJ Travers, EC Olivares, TA Clark, ...
Nature methods 7 (6), 461-465, 2010
17022010
Origins of the E. coli strain causing an outbreak of hemolytic–uremic syndrome in Germany
DA Rasko, DR Webster, JW Sahl, A Bashir, N Boisen, F Scheutz, ...
New England Journal of Medicine 365 (8), 709-717, 2011
10882011
The origin of the Haitian cholera outbreak strain
CS Chin, J Sorenson, JB Harris, WP Robins, RC Charles, ...
New England Journal of Medicine 364 (1), 33-42, 2011
9292011
A deep learning system for differential diagnosis of skin diseases
Y Liu, A Jain, C Eng, DH Way, K Lee, P Bui, K Kanada, ...
Nature medicine 26 (6), 900-908, 2020
6552020
Massively multitask networks for drug discovery
B Ramsundar, S Kearnes, P Riley, D Webster, D Konerding, V Pande
arXiv preprint arXiv:1502.02072, 2015
6182015
Towards expert-level medical question answering with large language models
K Singhal, T Tu, J Gottweis, R Sayres, E Wulczyn, L Hou, K Clark, S Pfohl, ...
arXiv preprint arXiv:2305.09617, 2023
5982023
Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy
J Krause, V Gulshan, E Rahimy, P Karth, K Widner, GS Corrado, L Peng, ...
Ophthalmology 125 (8), 1264-1272, 2018
5332018
Deep learning in ophthalmology: the technical and clinical considerations
DSW Ting, L Peng, AV Varadarajan, PA Keane, PM Burlina, MF Chiang, ...
Progress in retinal and eye research 72, 100759, 2019
4662019
Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy
R Sayres, A Taly, E Rahimy, K Blumer, D Coz, N Hammel, J Krause, ...
Ophthalmology 126 (4), 552-564, 2019
4202019
Towards generalist biomedical AI
T Tu, S Azizi, D Driess, M Schaekermann, M Amin, PC Chang, A Carroll, ...
NEJM AI 1 (3), AIoa2300138, 2024
2682024
Performance of a deep-learning algorithm vs manual grading for detecting diabetic retinopathy in India
V Gulshan, RP Rajan, K Widner, D Wu, P Wubbels, T Rhodes, ...
JAMA ophthalmology 137 (9), 987-993, 2019
2502019
A hybrid approach for the automated finishing of bacterial genomes
A Bashir, AA Klammer, WP Robins, CS Chin, D Webster, E Paxinos, ...
Nature biotechnology 30 (7), 701-707, 2012
2312012
Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program
P Ruamviboonsuk, J Krause, P Chotcomwongse, R Sayres, R Raman, ...
NPJ digital medicine 2 (1), 25, 2019
2282019
Detection of anaemia from retinal fundus images via deep learning
A Mitani, A Huang, S Venugopalan, GS Corrado, L Peng, DR Webster, ...
Nature biomedical engineering 4 (1), 18-27, 2020
2012020
Predicting the risk of developing diabetic retinopathy using deep learning
A Bora, S Balasubramanian, B Babenko, S Virmani, S Venugopalan, ...
The Lancet Digital Health 3 (1), e10-e19, 2021
1982021
Classification of nucleic acid templates
B Flusberg, S Turner, J Lee, L Jia, J Korlach, J Sorenson, D Webster, ...
US Patent App. 13/930,178, 2013
195*2013
Deep learning and glaucoma specialists: the relative importance of optic disc features to predict glaucoma referral in fundus photographs
S Phene, RC Dunn, N Hammel, Y Liu, J Krause, N Kitade, ...
Ophthalmology 126 (12), 1627-1639, 2019
1862019
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