21 citations · 25 across the 5 of their papers we have counts for
5 papers
Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium
Haris Shuaib, Gareth J Barker, Peter Sasieni +16
Objective: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep-learning models in gli…
Automated triaging of head MRI examinations using convolutional neural networks
David A. Wood, Sina Kafiabadi, Ayisha Al Busaidi +9
The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI…
Labelling imaging datasets on the basis of neuroradiology reports: a validation study
David A. Wood, Sina Kafiabadi, Aisha Al Busaidi +11
Natural language processing (NLP) shows promise as a means to automate the labelling of hospital-scale neuroradiology magnetic resonance imaging (MRI) datasets for computer vision…
Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM)
David A. Wood, Jeremy Lynch, Sina Kafiabadi +13
Labelling large datasets for training high-capacity neural networks is a major obstacle to the development of deep learning-based medical imaging applications. Here we present a tr…
Nonlinear functional mapping of the human brain
Nicholas Allgaier, Tobias Banaschewski, Gareth Barker +27
The field of neuroimaging has truly become data rich, and novel analytical methods capable of gleaning meaningful information from large stores of imaging data are in high demand.…