Seuraa
Sonia Phene
Sonia Phene
Vahvistettu sähköpostiosoite verkkotunnuksessa google.com
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Viittaukset
Viittaukset
Vuosi
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
209*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
1652019
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
402021
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
102021
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
82019
Deep Learning vs. Human Graders for Classifying Severity Levels of Diabetic Retinopathy in a Real-World Nationwide Screening Program. arXiv 2018
P Raumviboonsuk, J Krause, P Chotcomwongse, R Sayres, R Raman, ...
arXiv preprint arXiv:1810.08290, 0
8
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
52020
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, 2019
52019
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
22021
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
22019
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
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Artikkelit 1–17