| 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 | 4579 | 2016 |
| 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 | 1381 | 2010 |
| 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 | 1010 | 2018 |
| 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 | 982 | 2011 |
| 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 | 840 | 2011 |
| Massively multitask networks for drug discovery B Ramsundar, S Kearnes, P Riley, D Webster, D Konerding, V Pande arXiv preprint arXiv:1502.02072, 2015 | 470 | 2015 |
| 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 | 294 | 2018 |
| 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 | 214 | 2019 |
| 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 | 214 | 2012 |
| 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 | 211 | 2019 |
| 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 | 199 | 2020 |
| Distinguishing molecular features and clinical characteristics of a putative new rhinovirus species, human rhinovirus C (HRV C) P McErlean, LA Shackelton, E Andrews, DR Webster, SB Lambert, ... PloS one 3 (4), e1847, 2008 | 169 | 2008 |
| 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 | 160* | 2013 |
| Towards better measurement of attention and satisfaction in mobile search D Lagun, CH Hsieh, D Webster, V Navalpakkam Proceedings of the 37th international ACM SIGIR conference on Research …, 2014 | 142 | 2014 |
| 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 | 118 | 2019 |
| 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), 1-9, 2019 | 117 | 2019 |
| Genome-wide diversity and selective pressure in the human rhinovirus AL Kistler, DR Webster, S Rouskin, V Magrini, JJ Credle, DP Schnurr, ... Virology journal 4 (1), 1-19, 2007 | 103 | 2007 |
| 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 | 94 | 2019 |
| Deep learning for predicting refractive error from retinal fundus images AV Varadarajan, R Poplin, K Blumer, C Angermueller, J Ledsam, ... Investigative ophthalmology & visual science 59 (7), 2861-2868, 2018 | 94 | 2018 |
| 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 | 82 | 2020 |
Lily PengGoogle ResearchVerified email at google.com
