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Lauren Oakden-Rayner
Lauren Oakden-Rayner
Australian Institute for Machine Learning. University of Adelaide. Royal Adelaide Hospital.
Verified email at adelaide.edu.au - Homepage
Title
Cited by
Cited by
Year
Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension
X Liu, SC Rivera, D Moher, MJ Calvert, AK Denniston
bmj 370, 2020
2612020
Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
L Oakden-Rayner, J Dunnmon, G Carneiro, C Ré
Proceedings of the ACM conference on health, inference, and learning, 151-159, 2020
1572020
Precision radiology: predicting longevity using feature engineering and deep learning methods in a radiomics framework
L Oakden-Rayner, G Carneiro, T Bessen, JC Nascimento, AP Bradley, ...
Scientific reports 7 (1), 1-13, 2017
1242017
Deep learning predicts hip fracture using confounding patient and healthcare variables
MA Badgeley, JR Zech, L Oakden-Rayner, BS Glicksberg, M Liu, W Gale, ...
NPJ digital medicine 2 (1), 1-10, 2019
1142019
Exploring large-scale public medical image datasets
L Oakden-Rayner
Academic radiology 27 (1), 106-112, 2020
932020
Detecting hip fractures with radiologist-level performance using deep neural networks
W Gale, L Oakden-Rayner, G Carneiro, AP Bradley, LJ Palmer
arXiv preprint arXiv:1711.06504, 2017
832017
Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension
SC Rivera, X Liu, AW Chan, AK Denniston, MJ Calvert
Bmj 370, 2020
822020
Producing Radiologist-Quality Reports for Interpretable Deep Learning.
W Gale, L Oakden-Rayner, G Carneiro, LJ Palmer, AP Bradley
2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019 …, 2019
49*2019
Exploring the ChestXray14 dataset: problems
L Oakden-Rayner
Wordpress: Luke Oakden Rayner, 2017
482017
The false hope of current approaches to explainable artificial intelligence in health care
M Ghassemi, L Oakden-Rayner, AL Beam
The Lancet Digital Health 3 (11), e745-e750, 2021
462021
Deep learning natural language processing successfully predicts the cerebrovascular cause of transient ischemic attack-like presentations
S Bacchi, L Oakden-Rayner, T Zerner, T Kleinig, S Patel, J Jannes
Stroke 50 (3), 758-760, 2019
332019
Deep learning in the prediction of ischaemic stroke thrombolysis functional outcomes: a pilot study
S Bacchi, T Zerner, L Oakden-Rayner, T Kleinig, S Patel, J Jannes
Academic radiology 27 (2), e19-e23, 2020
292020
Reading Race: AI Recognises Patient's Racial Identity In Medical Images
I Banerjee, AR Bhimireddy, JL Burns, LA Celi, LC Chen, R Correa, ...
arXiv preprint arXiv:2107.10356, 2021
232021
Automated 5-year mortality prediction using deep learning and radiomics features from chest computed tomography
G Carneiro, L Oakden-Rayner, AP Bradley, J Nascimento, L Palmer
2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017 …, 2017
232017
The rebirth of CAD: how is modern AI different from the CAD we know?
L Oakden-Rayner
Radiology: artificial intelligence 1 (3), e180089, 2019
212019
A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology
J Scheetz, P Rothschild, M McGuinness, X Hadoux, HP Soyer, M Janda, ...
Scientific reports 11 (1), 1-10, 2021
192021
CheXNet: an in-depth review
L Oakden-Rayner
URL: https://lukeoakdenrayner. wordpress. com/2018/01/24/chexnetan-in-depth …, 2018
192018
Towards generative adversarial networks as a new paradigm for radiology education
SG Finlayson, H Lee, IS Kohane, L Oakden-Rayner
arXiv preprint arXiv:1812.01547, 2018
172018
Medical journals should embrace preprints to address the reproducibility crisis
L Oakden-Rayner, AL Beam, LJ Palmer
International Journal of Epidemiology 47 (5), 1363-1365, 2018
162018
Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study
JCY Seah, CHM Tang, QD Buchlak, XG Holt, JB Wardman, A Aimoldin, ...
The Lancet Digital Health 3 (8), e496-e506, 2021
132021
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