Lauren Oakden-Rayner
Lauren Oakden-Rayner
Inne imiona/nazwiskaLuke Oakden-Rayner
Australian Institute for Machine Learning. University of Adelaide. Royal Adelaide Hospital.
Zweryfikowany adres z adelaide.edu.au - Strona główna
Cytowane przez
Cytowane przez
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
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
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
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
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
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
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
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
Exploring the ChestXray14 dataset: problems
L Oakden-Rayner
Wordpress: Luke Oakden Rayner 1, 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
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
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
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
AI recognition of patient race in medical imaging: a modelling study
JW Gichoya, I Banerjee, AR Bhimireddy, JL Burns, LA Celi, LC Chen, ...
The Lancet Digital Health, 2022
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
The rebirth of CAD: how is modern AI different from the CAD we know?
L Oakden-Rayner
Radiology. Artificial intelligence 1 (3), 2019
The medical algorithmic audit
X Liu, B Glocker, MM McCradden, M Ghassemi, AK Denniston, ...
The Lancet Digital Health, 2022
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
CheXNet: an in-depth review
L Oakden-Rayner
URL: https://lukeoakdenrayner. wordpress. com/2018/01/24/chexnetan-in-depth …, 2018
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