| 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 | 162* | 2019 |
| 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 | 135 | 2019 |
| Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology B Alipanahi, F Hormozdiari, B Behsaz, J Cosentino, ZR McCaw, ... The American Journal of Human Genetics 108 (7), 1217-1230, 2021 | 27 | 2021 |
| Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. npj Digital Medicine 2, 1 (12 2019), 25 P Raumviboonsuk, J Krause, P Chotcomwongse, R Sayres, R Raman, ... DOI: http://dx. doi. org/10.1038/s41746-019-0099-8, 2019 | 8 | 2019 |
| Deep Learning vs P Raumviboonsuk, J Krause, P Chotcomwongse, R Sayres, R Raman, ... Human Graders for Classifying Severity Levels of Diabetic Retinopathy in a …, 2018 | 6 | 2018 |
| Improving medical annotation quality to decrease labeling burden using stratified noisy cross-validation J Hsu, S Phene, A Mitani, J Luo, N Hammel, J Krause, R Sayres arXiv preprint arXiv:2009.10858, 2020 | 5 | 2020 |
| Deep learning to assess glaucoma risk and associated features in fundus images S Phene, R Carter Dunn, N Hammel, Y Liu, J Krause, N Kitade arXiv preprint arXiv:1812.08911, 2018 | 5 | 2018 |
| Iterative quality control strategies for expert medical image labeling B Freeman, N Hammel, S Phene, A Huang, R Ackermann, O Kanzheleva, ... Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 9 …, 2021 | 4 | 2021 |
| A study of feature-based consensus formation for glaucoma risk assessment N Hammel, M Schaekermann, S Phene, C Dunn, L Peng, DR Webster, ... Investigative Ophthalmology & Visual Science 60 (9), 164-164, 2019 | 2 | 2019 |
| Lessons learnt from harnessing deep learning for real-world clinical applications in ophthalmology: detecting diabetic retinopathy from retinal fundus photographs Y Liu, L Yang, S Phene, L Peng Artificial Intelligence in Medicine, 247-264, 2021 | 1 | 2021 |
| Using Machine Learning-Based Trait Predictions For Genetic Association Discovery C McLean, B Alipanahi, J Cosentino, S Phene, A Carroll US Patent App. 17/770,174, 2022 | | 2022 |
| Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology AW Carroll, A Khawaja, B Alipanahi, B Behsaz, C McLean, D Sculley, ... | | 2021 |
| Re: Phene et al.: Deep learning and glaucoma specialists: the relative importance of optic disc features to predict glaucoma referral in fundus photographs (Ophthalmology. 2019 … S Phene, RC Dunn, N Kitade, Y Liu, N Hammel OPHTHALMOLOGY 127 (8), E58-E59, 2020 | | 2020 |
| Identifying and mitigating low-quality labels for deep learning in glaucoma J Hsu, S Phene, J Luo, A Mitani, N Hammel, J Krause, R Sayres Investigative Ophthalmology & Visual Science 61 (7), 4537-4537, 2020 | | 2020 |
| Performance of Deep Learning Glaucoma Suspect Models Compared to Various Reference Standards L Yang, C Dunn, AE Huang, N Hammel, I Traynis, M Gandhi, J Krause, ... Investigative Ophthalmology & Visual Science 61 (7), 4538-4538, 2020 | | 2020 |
| Identifying glaucomatous optic nerve head features and glaucoma risk in fundus images at eye-care provider levels of accuracy using deep learning algorithms S Phene, N Hammel, AE Huang, AY Maa, C Dunn, C Semturs, L Peng, ... Investigative Ophthalmology & Visual Science 60 (9), 1460-1460, 2019 | | 2019 |
| Translating Norms to the Digital Age D Kehl, T Maurer, S Phene | | 2013 |
