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Luke Oakden-Rayner

Showing results (11-20 of 21) with videos related to

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Journal of Clinical Neuroscience : Official Journal of the Neurosurgical Society of Australasia|October 19, 2020
Stroke prognostication for discharge planning with machine learning: A derivation studyStephen Bacchi, Luke Oakden-Rayner, David K Menon, et al.
Scientific Reports|May 12, 2017
Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics frameworkLuke Oakden-Rayner, Gustavo Carneiro, Taryn Bessen, et al.
Journal of Clinical Neuroscience : Official Journal of the Neurosurgical Society of Australasia|January 9, 2022
Prospective and external validation of stroke discharge planning machine learning modelsStephen Bacchi, Luke Oakden-Rayner, David K Menon, et al.
Journal of Medical Imaging and Radiation Oncology|June 25, 2021
Chest radiographs and machine learning - Past, present and futureCatherine M Jones, Quinlan D Buchlak, Luke Oakden-Rayner, et al.
Journal of Clinical Neuroscience : Official Journal of the Neurosurgical Society of Australasia|October 26, 2019
Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: A pilot studyStephen Bacchi, Toby Zerner, John Dongas, et al.
BMJ Open|December 21, 2021
Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational studyCatherine M Jones, Luke Danaher, Michael R Milne, et al.
BMJ Open|December 8, 2021
Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiographyJarrel Seah, Cyril Tang, Quinlan D Buchlak, et al.
NPJ Digital Medicine|July 16, 2019
Deep learning predicts hip fracture using confounding patient and healthcare variablesMarcus A Badgeley, John R Zech, Luke Oakden-Rayner, et al.
Scientific Reports|March 5, 2021
A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncologyJane Scheetz, Philip Rothschild, Myra McGuinness, et al.
Internal Medicine Journal|September 20, 2021
Medical student knowledge and critical appraisal of machine learning: a multicentre international cross-sectional studyCharlotte Blacketer, Roger Parnis, Kyle B Franke, et al.
Pageof 3

Showing results (11-20 of 21) with videos related to

Sort By:
Pageof 3
Journal of Clinical Neuroscience : Official Journal of the Neurosurgical Society of Australasia|October 19, 2020
Stroke prognostication for discharge planning with machine learning: A derivation studyStephen Bacchi, Luke Oakden-Rayner, David K Menon, et al.
Scientific Reports|May 12, 2017
Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics frameworkLuke Oakden-Rayner, Gustavo Carneiro, Taryn Bessen, et al.
Journal of Clinical Neuroscience : Official Journal of the Neurosurgical Society of Australasia|January 9, 2022
Prospective and external validation of stroke discharge planning machine learning modelsStephen Bacchi, Luke Oakden-Rayner, David K Menon, et al.
Journal of Medical Imaging and Radiation Oncology|June 25, 2021
Chest radiographs and machine learning - Past, present and futureCatherine M Jones, Quinlan D Buchlak, Luke Oakden-Rayner, et al.
Journal of Clinical Neuroscience : Official Journal of the Neurosurgical Society of Australasia|October 26, 2019
Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: A pilot studyStephen Bacchi, Toby Zerner, John Dongas, et al.
BMJ Open|December 21, 2021
Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational studyCatherine M Jones, Luke Danaher, Michael R Milne, et al.
BMJ Open|December 8, 2021
Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiographyJarrel Seah, Cyril Tang, Quinlan D Buchlak, et al.
NPJ Digital Medicine|July 16, 2019
Deep learning predicts hip fracture using confounding patient and healthcare variablesMarcus A Badgeley, John R Zech, Luke Oakden-Rayner, et al.
Scientific Reports|March 5, 2021
A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncologyJane Scheetz, Philip Rothschild, Myra McGuinness, et al.
Internal Medicine Journal|September 20, 2021
Medical student knowledge and critical appraisal of machine learning: a multicentre international cross-sectional studyCharlotte Blacketer, Roger Parnis, Kyle B Franke, et al.
Pageof 3