Luke Oakden-Rayner
Luke Oakden-Rayner
Australian Institute for Machine Learning. University of Adelaide. Royal Adelaide Hospital.
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
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
Exploring large-scale public medical image datasets
L Oakden-Rayner
Academic radiology 27 (1), 106-112, 2020
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
Exploring the ChestXray14 dataset: problems
L Oakden-Rayner
Wordpress: Luke Oakden Rayner, 2017
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
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
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
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
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
The rebirth of CAD: how is modern AI different from the CAD we know?
L Oakden-Rayner
Radiology: artificial intelligence 1 (3), e180089, 2019
CheXNet: an in-depth review
L Oakden-Rayner
URL: https://lukeoakdenrayner. wordpress. com/2018/01/24/chexnetan-in-depth …, 2018
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
Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: a pilot study
S Bacchi, T Zerner, J Dongas, AT Asahina, A Abou-Hamden, S Otto, ...
Journal of Clinical Neuroscience 70, 11-13, 2019
Artificial intelligence in medicine: validation and study design
L Oakden-Rayner, LJ Palmer
Artificial Intelligence in Medical Imaging, 83-104, 2019
Intelligent augmented reality tutoring for physical tasks with medical professionals
MA Almiyad, L Oakden-Rayner, A Weerasinghe, M Billinghurst
International Conference on Artificial Intelligence in Education, 450-454, 2017
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
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